db_biii_perso.sql

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-- Structure de la table `academicpaper`
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CREATE TABLE `academicpaper` (
  `id_paper` decimal(10,0) NOT NULL DEFAULT '0',
  `id_type` decimal(10,0) DEFAULT NULL,
  `Title` varchar(255) DEFAULT NULL,
  `Journal` varchar(255) DEFAULT NULL,
  `Volume` varchar(255) DEFAULT NULL,
  `Number` varchar(255) DEFAULT NULL,
  `Pages` varchar(255) DEFAULT NULL,
  `Year` decimal(10,0) DEFAULT NULL,
  `ISSN` varchar(255) DEFAULT NULL,
  `doi` varchar(255) DEFAULT NULL,
  `Abstract` longtext,
  `Date` varchar(255) DEFAULT NULL,
  `URL` varchar(255) DEFAULT NULL,
  `ISBN` varchar(255) DEFAULT NULL,
  `Issue` varchar(255) DEFAULT NULL,
  `Month` decimal(10,0) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

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-- Contenu de la table `academicpaper`
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INSERT INTO `academicpaper` (`id_paper`, `id_type`, `Title`, `Journal`, `Volume`, `Number`, `Pages`, `Year`, `ISSN`, `doi`, `Abstract`, `Date`, `URL`, `ISBN`, `Issue`, `Month`) VALUES
('2737', '102', 'Experimenters guide to colocalization studies finding a way thro', 'Methods Cell Biol', '123', NULL, '395-408', '2014', '0091-679X', '10.1016/B978-0-12-420138-5.00021-5', '<p>Multicolor fluorescence microscopy helps to define the local interplay of subcellular components in cell biological experiments. The analysis of spatial coincidence of two or more markers is a first step in investigating the potential interactions of molecular actors. Colocalization studies rely on image preprocessing and further analysis; however, they are limited by optical resolution. Once those limitations are taken into account, characterization might be performed. In this review, we discuss two types of parameters that are aimed at evaluating colocalization, which are indicators and quantifiers. Indicators evaluate signal coincidence over a predefined scale, while quantifiers provide an absolute measurement. As the image is both a collection of intensities and a collection of objects, both approaches are applicable. Most of the available image processing software include various colocalization options; however, guidance for the choice of the appropriate method is rarely proposed. In this review, we provide the reader with a basic description of the available colocalization approaches, proposing a guideline for their use, either alone or in combination.</p>', '2014', '', '', '', NULL),
('2760', '102', 'Autofocusing in computer microscopy selecting the optimal focus ', 'Microsc Res Tech', '65', NULL, '139-49', '2004', '1059-910X', '10.1002/jemt.20118', '<p>Autofocusing is a fundamental technology for automated biological and biomedical analyses and is indispensable for routine use of microscopes on a large scale. This article presents a comprehensive comparison study of 18 focus algorithms in which a total of 139,000 microscope images were analyzed. Six samples were used with three observation methods (brightfield, phase contrast, and differential interference contrast (DIC)) under two magnifications (100x and 400x). A ranking methodology is proposed, based on which the 18 focus algorithms are ranked. Image preprocessing was also conducted to extensively reveal the performance and robustness of the focus algorithms. The presented guidelines allow for the selection of the optimal focus algorithm for different microscopy applications.</p>', '2004 Oct', '', '', '3', NULL),
('2775', '102', 'Beyond colocalization inferring spatial interactions between sub', 'BMC Bioinformatics', '11', NULL, '372', '2010', '1471-2105', '10.1186/1471-2105-11-372', '<p><b>BACKGROUND: </b>Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on spatial proximity, other interactions typically result in spatial correlations between the interacting structures. Such correlations are the target of microscopy-based co-localization analysis, which can provide hints of potential interactions. Two complementary approaches to co-localization analysis can be distinguished: intensity correlation methods capitalize on pattern discovery, whereas object-based methods emphasize detection power.</p><p><b>RESULTS: </b>We first reinvestigate the classical co-localization measure in the context of spatial point pattern analysis. This allows us to unravel the set of implicit assumptions inherent to this measure and to identify potential confounding factors commonly ignored. We generalize object-based co-localization analysis to a statistical framework involving spatial point processes. In this framework, interactions are understood as position co-dependencies in the observed localization patterns. The framework is based on a model of effective pairwise interaction potentials and the specification of a null hypothesis for the expected pattern in the absence of interaction. Inferred interaction potentials thus reflect all significant effects that are not explained by the null hypothesis. Our model enables the use of a wealth of well-known statistical methods for analyzing experimental data, as demonstrated on synthetic data and in a case study considering virus entry into live cells. We show that the classical co-localization measure typically under-exploits the information contained in our data.</p><p><b>CONCLUSIONS: </b>We establish a connection between co-localization and spatial interaction of sub-cellular structures by formulating the object-based interaction analysis problem in a spatial statistics framework based on nearest-neighbor distance distributions. We provide generic procedures for inferring interaction strengths and quantifying their relative statistical significance from sets of discrete objects as provided by image analysis methods. Within our framework, an interaction potential can either refer to a phenomenological or a mechanistic model of a physico-chemical interaction process. This increased flexibility in designing and testing different hypothetical interaction models can be used to quantify the parameters of a specific interaction model or may catalyze the discovery of functional relations.</p>', '2010', '', '', '', NULL),
('2777', '102', 'Using CellX to quantify intracellular events', 'Curr Protoc Mol Biol', 'Chapter 14', NULL, 'Unit 14.22.', '2013', '1934-3647', '10.1002/0471142727.mb1422s101', '<p>Methods to quantify features of individual cells using light microscopy have become widely used in biology. A multitude of computational tools has been developed for image analysis; however, they are often only for specific cell types and microscopy techniques. This unit describes CellX, an open-source software package for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images. CellX can perform cell segmentation largely independently of cell shapes, and can also cope with images that are crowded with cells. The basic protocol describes how to use CellX for cell segmentation and quantification. This protocol remains the same whether there is a collection of images to be analyzed or whether cell tracking on a sequence of images is to be performed. The CellX output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.</p>', '2013', '', '', '', NULL),
('2778', '102', 'Avoiding twisted pixels ethical guidelines for the appropriate u', 'Sci Eng Ethics', '16', NULL, '639-67', '2010', '1471-5546', '10.1007/s11948-010-9201-y', '<p>Digital imaging has provided scientists with new opportunities to acquire and manipulate data using techniques that were difficult or impossible to employ in the past. Because digital images are easier to manipulate than film images, new problems have emerged. One growing concern in the scientific community is that digital images are not being handled with sufficient care. The problem is twofold: (1) the very small, yet troubling, number of intentional falsifications that have been identified, and (2) the more common unintentional, inappropriate manipulation of images for publication. Journals and professional societies have begun to address the issue with specific digital imaging guidelines. Unfortunately, the guidelines provided often do not come with instructions to explain their importance. Thus they deal with what should or should not be done, but not the associated \'why\' that is required for understanding the rules. This article proposes 12 guidelines for scientific digital image manipulation and discusses the technical reasons behind these guidelines. These guidelines can be incorporated into lab meetings and graduate student training in order to provoke discussion and begin to bring an end to the culture of "data beautification".</p>', '2010 Dec', '', '', '4', NULL),
('2782', '102', 'Momentpreserving thresolding A new approach', 'Computer Vision, Graphics, and Image Processing', '29', NULL, '377 - 393', '1985', '0734-189X', 'http://dx.doi.org/10.1016/0734-189X(85)90133-1', 'A new approach to automatic threshold selection using the moment-preserving principle is proposed. The threshold values are computed deterministically in such a way that the moments of an input picture is preserved in the output picture. Experimental results show that the approach can be employed to threshold a given picture into meaningful gray-level classes. The approach is described for global thresholding, but it is applicable to local thresholding as well.', '', 'http://www.sciencedirect.com/science/article/pii/0734189X85901331', '', '', NULL),
('2784', '102', 'Huygens Remote Manager A Web Interface for HighVolume Batch Deco', 'Imaging & Microscopy', '9', NULL, '57–58', '2007', '14394243, 18637809', '10.1002/imic.200790154', '', '', 'http://doi.wiley.com/10.1002/imic.200790154', '', '', NULL),
('2787', '102', 'Quantification of histochemical staining by color deconvolution', 'Anal Quant Cytol Histol', '23', NULL, '291-9', '2001', '0884-6812', '', '<p><b>OBJECTIVE: </b>To develop a flexible method of separation and quantification of immunohistochemical staining by means of color image analysis.</p><p><b>STUDY DESIGN: </b>An algorithm was developed to deconvolve the color information acquired with red-green-blue (RGB) cameras and to calculate the contribution of each of the applied stains based on stain-specific RGB absorption. The algorithm was tested using different combinations of diaminobenzidine, hematoxylin and eosin at different staining levels.</p><p><b>RESULTS: </b>Quantification of the different stains was not significantly influenced by the combination of multiple stains in a single sample. The color deconvolution algorithm resulted in comparable quantification independent of the stain combinations as long as the histochemical procedures did not influence the amount of stain in the sample due to bleaching because of stain solubility and saturation of staining was prevented.</p><p><b>CONCLUSION: </b>This image analysis algorithm provides a robust and flexible method for objective immunohistochemical analysis of samples stained with up to three different stains using a laboratory microscope, standard RGB camera setup and the public domain program NIH Image.</p>', '2001 Aug', '', '', '4', NULL),
('2789', '102', 'ImmunoRatio a publicly available web application for quantitativ', 'Breast Cancer Res', '12', NULL, 'R56', '2010', '1465-542X', '10.1186/bcr2615', '<p><b>INTRODUCTION: </b>Accurate assessment of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 is essential in the histopathologic diagnostics of breast cancer. Commercially available image analysis systems are usually bundled with dedicated analysis hardware and, to our knowledge, no easily installable, free software for immunostained slide scoring has been described. In this study, we describe a free, Internet-based web application for quantitative image analysis of ER, PR, and Ki-67 immunohistochemistry in breast cancer tissue sections.</p><p><b>METHODS: </b>The application, named ImmunoRatio, calculates the percentage of positively stained nuclear area (labeling index) by using a color deconvolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. ImmunoRatio was calibrated using cell counts defined visually as the gold standard (training set, n = 50). Validation was done using a separate set of 50 ER, PR, and Ki-67 stained slides (test set, n = 50). In addition, Ki-67 labeling indexes determined by ImmunoRatio were studied for their prognostic value in a retrospective cohort of 123 breast cancer patients.</p><p><b>RESULTS: </b>The labeling indexes by calibrated ImmunoRatio analyses correlated well with those defined visually in the test set (correlation coefficient r = 0.98). Using the median Ki-67 labeling index (20%) as a cutoff, a hazard ratio of 2.2 was obtained in the survival analysis (n = 123, P = 0.01). ImmunoRatio was shown to adapt to various staining protocols, microscope setups, digital camera models, and image acquisition settings. The application can be used directly with web browsers running on modern operating systems (e.g., Microsoft Windows, Linux distributions, and Mac OS). No software downloads or installations are required. ImmunoRatio is open source software, and the web application is publicly accessible on our website.</p><p><b>CONCLUSIONS: </b>We anticipate that free web applications, such as ImmunoRatio, will make the quantitative image analysis of ER, PR, and Ki-67 easy and straightforward in the diagnostic assessment of breast cancer specimens.</p>', '2010', '', '', '4', NULL),
('2792', '102', 'Biological imaging software tools', 'Nat Methods', '9', NULL, '697-710', '2012', '1548-7105', '10.1038/nmeth.2084', '<p>Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.</p>', '2012 Jul', '', '', '7', NULL),
('2793', '102', 'Ncadherin and ?1integrins cooperate during the development of th', 'Dev Biol', '364', NULL, '178-91', '2012', '1095-564X', '10.1016/j.ydbio.2012.02.001', '<p>Cell adhesion controls various embryonic morphogenetic processes, including the development of the enteric nervous system (ENS). Ablation of ?1-integrin (?1-/-) expression in enteric neural crest cells (ENCC) in mice leads to major alterations in the ENS structure caused by reduced migration and increased aggregation properties of ENCC during gut colonization, which gives rise to a Hirschsprung\'s disease-like phenotype. In the present study, we examined the role of N-cadherin in ENS development and the interplay with ?1 integrins during this process. The Ht-PA-Cre mouse model was used to target gene disruption of N-cadherin and ?1 integrin in migratory NCC and to produce single- and double-conditional mutants for these two types of adhesion receptors. Double mutation of N-cadherin and ?1 integrin led to embryonic lethality with severe defects in ENS development. N-cadherin-null (Ncad-/-) ENCC exhibited a delayed colonization in the developing gut at E12.5, although this was to a lesser extent than in ?1-/- mutants. This delay of Ncad-/- ENCC migration was recovered at later stages of development. The double Ncad-/-; ?1-/- mutant ENCC failed to colonize the distal part of the gut and there was more severe aganglionosis in the proximal hindgut than in the single mutants for N-cadherin or ?1-integrin. This was due to an altered speed of locomotion and directionality in the gut wall. The abnormal aggregation defect of ENCC and the disorganized ganglia network in the ?1-/- mutant was not observed in the double mutant. This indicates that N-cadherin enhances the effect of the ?1-integrin mutation and demonstrates cooperation between these two adhesion receptors during ENS ontogenesis. In conclusion, our data reveal that N-cadherin is not essential for ENS development but it does modulate the modes of ENCC migration and acts in concert with ?1-integrin to control the proper development of the ENS.</p>', '2012 Apr 15', '', '', '2', NULL),
('2796', '102', 'Computational imaging in cell biology', 'J Cell Biol', '161', NULL, '477-81', '2003', '0021-9525', '10.1083/jcb.200302097', '<p>Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today\'s imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.</p>', '2003 May 12', '', '', '3', NULL),
('2798', '102', 'SparkMaster automated calcium spark analysis with ImageJ', 'Am J Physiol Cell Physiol', '293', NULL, 'C1073-81', '2007', '0363-6143', '10.1152/ajpcell.00586.2006', '<p>Ca sparks are elementary Ca-release events from intracellular Ca stores that are observed in virtually all types of muscle. Typically, Ca sparks are measured in the line-scan mode with confocal laser-scanning microscopes, yielding two-dimensional images (distance vs. time). The manual analysis of these images is time consuming and prone to errors as well as investigator bias. Therefore, we developed SparkMaster, an automated analysis program that allows rapid and reliable spark analysis. The underlying analysis algorithm is adapted from the threshold-based standard method of spark analysis developed by Cheng et al. (Biophys J 76: 606-617, 1999) and is implemented here in the freely available image-processing software ImageJ. SparkMaster offers a graphical user interface through which all analysis parameters and output options are selected. The analysis includes general image parameters (number of detected sparks, spark frequency) and individual spark parameters (amplitude, full width at half-maximum amplitude, full duration at half-maximum amplitude, full width, full duration, time to peak, maximum steepness of spark upstroke, time constant of spark decay). We validated the algorithm using images with synthetic sparks embedded into backgrounds with different signal-to-noise ratios to determine an analysis criteria at which a high sensitivity is combined with a low frequency of false-positive detections. Finally, we applied SparkMaster to analyze experimental data of sparks measured in intact and permeabilized ventricular cardiomyocytes, permeabilized mammalian skeletal muscle, and intact smooth muscle cells. We found that SparkMaster provides a reliable, easy to use, and fast way of analyzing Ca sparks in a wide variety of experimental conditions.</p>', '2007 Sep', '', '', '3', NULL),
('2800', '102', 'Amplitude distribution of calcium sparks in confocal images theo', 'Biophys J', '76', NULL, '606-17', '1999', '0006-3495', '10.1016/S0006-3495(99)77229-2', '<p>Determination of the calcium spark amplitude distribution is of critical importance for understanding the nature of elementary calcium release events in striated muscle. In the present study we show, on general theoretical grounds, that calcium sparks, as observed in confocal line scan images, should have a nonmodal, monotonic decreasing amplitude distribution, regardless of whether the underlying events are stereotyped. To test this prediction we developed, implemented, and verified an automated computer algorithm for objective detection and measurement of calcium sparks in raw image data. When the sensitivity and reliability of the algorithm were set appropriately, we observed highly left-skewed or monotonic decreasing amplitude distributions in skeletal muscle cells and cardiomyocytes, confirming the theoretical predictions. The previously reported modal or Gaussian distributions of sparks detected by eye must therefore be the result of subjective detection bias against small amplitude events. In addition, we discuss possible situations when a modal distribution might be observed.</p>', '1999 Feb', '', '', '2', NULL),
('2804', '102', 'Why join groups Lessons from parasitemanipulated Artemia', 'Ecol Lett', '16', NULL, '493-501', '2013', '1461-0248', '10.1111/ele.12074', '<p>Grouping behaviours (e.g. schooling, shoaling and swarming) are commonly explicated through adaptive hypotheses such as protection against predation, access to mates or improved foraging. However, the hypothesis that aggregation can result from manipulation by parasites to increase their transmission has never been demonstrated. We investigated this hypothesis using natural populations of two crustacean hosts (Artemia franciscana and Artemia parthenogenetica) infected with one cestode and two microsporidian parasites. We found that swarming propensity increased in cestode-infected hosts and that red colour intensity was higher in swarming compared with non-swarming infected hosts. These effects likely result in increased cestode transmission to its final avian host. Furthermore, we found that microsporidian-infected hosts had both increased swarming propensity and surfacing behaviour. Finally, we demonstrated using experimental infections that these concurrent manipulations result in increased spore transmission to new hosts. Hence, this study suggests that parasites can play a prominent role in host grouping behaviours.</p>', '2013 Apr', '', '', '4', NULL),
('2811', '102', 'Simple system using natural mineral water for highthroughput phe', 'International Journal of High Throughput Screening', '', NULL, '1', '2013', '1179-1381', '10.2147/IJHTS.S40565', '', '', 'http://www.dovepress.com/simple-system-using-natural-mineral-water-for-high-throughput-phenotyp-peer-reviewed-article-IJHTS', '', '', NULL),
('2812', '102', 'Quickandclean article figures with FigureJ', 'J Microsc', '252', NULL, '89-91', '2013', '1365-2818', '10.1111/jmi.12069', '<p>We created FigureJ a new ImageJ plugin dedicated to scientific article figures preparation. Building a convincing figure is a demanding task that covers different steps ranging from content acquisition to figure assembly in editing software. Notions of image processing are required when it comes to even simple tasks such as cropping or resizing images and assembling them in a single figure. Scientific images are typically well handled in dedicated software but poorly supported in software used for laying out the final version of a figure for submission to review process.</p>', '2013 Oct', '', '', '1', NULL),
('2817', '103', 'ImageJ Macro Tool Sets for Biological Image Analysis', 'ImageJ User and Developer Conference 2012', 'None', NULL, 'None', '2012', 'None', '', '', '', '', '2-919941-18-6', 'None', NULL),
('2818', '102', 'ImmunoMembrane a publicly available web application for digital ', 'Histopathology', '60', NULL, '758-67', '2012', '1365-2559', '10.1111/j.1365-2559.2011.04142.x', '<p><b>AIMS: </b>Assessment of the human epidermal growth factor receptor 2 (HER2) with immunohistochemistry (IHC) is routine practice in clinical pathology laboratories. Visual classification of the staining reaction (usually into 0/1+, 2+ or 3+) is subjective and prone to significant inter- and intra-observer variation. In this study, we describe ImmunoMembrane, an easy-to-use HER2 IHC analysis software, which is freely available as a web application, requiring no download or installation.</p><p><b>METHODS AND RESULTS: </b>ImmunoMembrane uses colour deconvolution for stain separation and a customized algorithm for cell membrane segmentation. A quantitative score (IM-score, 0-20 points) is generated according to the membrane staining intensity and completeness. Specimens are classified into 0/1+, 2+ or 3+ based on IM-score cut-offs defined using a training set. The classification and membrane segmentation are presented as a pseudo-coloured overlay image. With a validation set (144 HercepTest(®) -stained whole tissue sections), ImmunoMembrane matched well with the pathologist\'s visual classification (weighted kappa ?(w) =0.80), as well as fluorescence in-situ hybridization (FISH) (IHC disagreement 3.5%, n=144) and chromogenic in-situ hybridization (CISH) (IHC disagreement 2.8%, n=144).</p><p><b>CONCLUSIONS: </b>We anticipate that publicly available web applications, such as ImmunoMembrane, will accelerate the adoption of automated image analysis in clinical diagnostics of HER2 IHC. ImmunoMembrane is freely accessible at: http://jvsmicroscope.uta.fi/immunomembrane/.</p>', '2012 Apr', '', '', '5', NULL),
('2830', '102', 'Robust singleparticle tracking in livecell timelapse sequences', 'Nat Methods', '5', NULL, '695-702', '2008', '1548-7105', '10.1038/nmeth.1237', '<p>Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.</p>', '2008 Aug', '', '', '8', NULL),
('2831', '102', 'Automated cell tracking and analysis in phasecontrast videos iTr', 'PLoS One', '8', NULL, 'e81266', '2013', '1932-6203', '10.1371/journal.pone.0081266', '<p>Cell migration is a key biological process with a role in both physiological and pathological conditions. Locomotion of cells during embryonic development is essential for their correct positioning in the organism; immune cells have to migrate and circulate in response to injury. Failure of cells to migrate or an inappropriate acquisition of migratory capacities can result in severe defects such as altered pigmentation, skull and limb abnormalities during development, and defective wound repair, immunosuppression or tumor dissemination. The ability to accurately analyze and quantify cell migration is important for our understanding of development, homeostasis and disease. In vitro cell tracking experiments, using primary or established cell cultures, are often used to study migration as cells can quickly and easily be genetically or chemically manipulated. Images of the cells are acquired at regular time intervals over several hours using microscopes equipped with CCD camera. The locations (x,y,t) of each cell on the recorded sequence of frames then need to be tracked. Manual computer-assisted tracking is the traditional method for analyzing the migratory behavior of cells. However, this processing is extremely tedious and time-consuming. Most existing tracking algorithms require experience in programming languages that are unfamiliar to most biologists. We therefore developed an automated cell tracking program, written in Java, which uses a mean-shift algorithm and ImageJ as a library. iTrack4U is a user-friendly software. Compared to manual tracking, it saves considerable amount of time to generate and analyze the variables characterizing cell migration, since they are automatically computed with iTrack4U. Another major interest of iTrack4U is the standardization and the lack of inter-experimenter differences. Finally, iTrack4U is adapted for phase contrast and fluorescent cells.</p>', '2013', '', '', '11', NULL),
('2839', '103', 'method for normalizing histology slides for quantitative analysi', 'Biomedical Imaging: From Nano to Macro, 2009. ISBI \'09. IEEE International Symposium on', 'None', NULL, 'None', '2009', 'None', '10.1109/ISBI.2009.5193250', 'Inconsistencies in the preparation of histology slides make it difficult to perform quantitative analysis on their results. In this paper we provide two mechanisms for overcoming many of the known inconsistencies in the staining process, thereby bringing slides that were processed or stored under very different conditions into a common, normalized space to enable improved quantitative analysis.', 'June', '', '', 'None', NULL),
('2850', '102', 'PAR1dependent orientation gradient of dynamic microtubules direc', 'J Cell Biol', '194', NULL, '121-35', '2011', '1540-8140', '10.1083/jcb.201103160', '<p>Cytoskeletal organization is central to establishing cell polarity in various cellular contexts, including during messenger ribonucleic acid sorting in Drosophila melanogaster oocytes by microtubule (MT)-dependent molecular motors. However, MT organization and dynamics remain controversial in the oocyte. In this paper, we use rapid multichannel live-cell imaging with novel image analysis, tracking, and visualization tools to characterize MT polarity and dynamics while imaging posterior cargo transport. We found that all MTs in the oocyte were highly dynamic and were organized with a biased random polarity that increased toward the posterior. This organization originated through MT nucleation at the oocyte nucleus and cortex, except at the posterior end of the oocyte, where PAR-1 suppressed nucleation. Our findings explain the biased random posterior cargo movements in the oocyte that establish the germline and posterior.</p>', '2011 Jul 11', '', '', '1', NULL),
('2871', '102', 'PALMsiever a tool to turn raw data into results for singlemolecu', 'Bioinformatics', '', NULL, '', '2014', '1367-4811', '10.1093/bioinformatics/btu720', '<p>During the past decade, localization microscopy (LM) has transformed into an accessible, commercially available technique for life sciences. However, data processing can be challenging to the non-specialist and care is still needed to produce meaningful results. PALMsiever has been developed to provide a user-friendly means of visualizing, filtering and analyzing LM data. It includes drift correction, clustering, intelligent line profiles, many rendering algorithms and 3D data visualization. It incorporates the main analysis and data processing modalities used by experts in the field, as well as several new features we developed, and makes them broadly accessible. It can easily be extended via plugins and is provided as free of charge open-source software.</p><p><b>CONTACT: </b>thomas.pengo@gmail.com.</p>', '2014 Oct 31', '', '', '', NULL),
('2882', '102', 'Arabidopsis plants acclimate to water deficit at low cost throug', 'Plant Physiology', '154', NULL, '357–372', '2010', '1532-2548', '10.1104/pp.110.157008', 'Growth and carbon (C) fluxes are severely altered in plants exposed to soil water deficit. Correspondingly, it has been suggested that plants under water deficit suffer from C shortage. In this study, we test this hypothesis in Arabidopsis (Arabidopsis thaliana) by providing an overview of the responses of growth, C balance, metabolites, enzymes of the central metabolism, and a set of sugar-responsive genes to a sustained soil water deficit. The results show that under drought, rosette relative expansion rate is decreased more than photosynthesis, leading to a more positive C balance, while root growth is promoted. Several soluble metabolites accumulate in response to soil water deficit, with K(+) and organic acids as the main contributors to osmotic adjustment. Osmotic adjustment costs only a small percentage of the daily photosynthetic C fixation. All C metabolites measured (not only starch and sugars but also organic acids and amino acids) show a diurnal turnover that often increased under water deficit, suggesting that these metabolites are readily available for being metabolized in situ or exported to roots. On the basis of 30 enzyme activities, no in-depth reprogramming of C metabolism was observed. Water deficit induces a shift of the expression level of a set of sugar-responsive genes that is indicative of increased, rather than decreased, C availability. These results converge to show that the differential impact of soil water deficit on photosynthesis and rosette expansion results in an increased availability of C for the roots, an increased turnover of C metabolites, and a low-cost C-based osmotic adjustment, and these responses are performed without major reformatting of the primary metabolism machinery.', '', 'http://www.ncbi.nlm.nih.gov/pubmed/20631317', '', '', NULL),
('2886', '102', 'Towards realtime image deconvolution application to confocal and', 'Sci Rep', '3', NULL, '2523', '2013', '2045-2322', '10.1038/srep02523', '<p>Although deconvolution can improve the quality of any type of microscope, the high computational time required has so far limited its massive spreading. Here we demonstrate the ability of the scaled-gradient-projection (SGP) method to provide accelerated versions of the most used algorithms in microscopy. To achieve further increases in efficiency, we also consider implementations on graphic processing units (GPUs). We test the proposed algorithms both on synthetic and real data of confocal and STED microscopy. Combining the SGP method with the GPU implementation we achieve a speed-up factor from about a factor 25 to 690 (with respect the conventional algorithm). The excellent results obtained on STED microscopy images demonstrate the synergy between super-resolution techniques and image-deconvolution. Further, the real-time processing allows conserving one of the most important property of STED microscopy, i.e the ability to provide fast sub-diffraction resolution recordings.</p>', '2013', '', '', '', NULL),
('2897', '102', 'Segmentation and quantification of subcellular structures in flu', 'Nat Protoc', '9', NULL, '586-96', '2014', '1750-2799', '10.1038/nprot.2014.037', '<p>Detection and quantification of fluorescently labeled molecules in subcellular compartments is a key step in the analysis of many cell biological processes. Pixel-wise colocalization analyses, however, are not always suitable, because they do not provide object-specific information, and they are vulnerable to noise and background fluorescence. Here we present a versatile protocol for a method named \'Squassh\' (segmentation and quantification of subcellular shapes), which is used for detecting, delineating and quantifying subcellular structures in fluorescence microscopy images. The workflow is implemented in freely available, user-friendly software. It works on both 2D and 3D images, accounts for the microscope optics and for uneven image background, computes cell masks and provides subpixel accuracy. The Squassh software enables both colocalization and shape analyses. The protocol can be applied in batch, on desktop computers or computer clusters, and it usually requires <1 min and <5 min for 2D and 3D images, respectively. Basic computer-user skills and some experience with fluorescence microscopy are recommended to successfully use the protocol.</p>', '2014 Mar', '', '', '3', NULL),
('2901', '102', 'Automated detection and measurement of isolated retinal arteriol', 'PLoS One', '9', NULL, 'e91791', '2014', '1932-6203', '10.1371/journal.pone.0091791', '<p>Pressure myography studies have played a crucial role in our understanding of vascular physiology and pathophysiology. Such studies depend upon the reliable measurement of changes in the diameter of isolated vessel segments over time. Although several software packages are available to carry out such measurements on small arteries and veins, no such software exists to study smaller vessels (<50 µm in diameter). We provide here a new, freely available open-source algorithm, MyoTracker, to measure and track changes in the diameter of small isolated retinal arterioles. The program has been developed as an ImageJ plug-in and uses a combination of cost analysis and edge enhancement to detect the vessel walls. In tests performed on a dataset of 102 images, automatic measurements were found to be comparable to those of manual ones. The program was also able to track both fast and slow constrictions and dilations during intraluminal pressure changes and following application of several drugs. Variability in automated measurements during analysis of videos and processing times were also investigated and are reported. MyoTracker is a new software to assist during pressure myography experiments on small isolated retinal arterioles. It provides fast and accurate measurements with low levels of noise and works with both individual images and videos. Although the program was developed to work with small arterioles, it is also capable of tracking the walls of other types of microvessels, including venules and capillaries. It also works well with larger arteries, and therefore may provide an alternative to other packages developed for larger vessels when its features are considered advantageous.</p>', '2014', '', '', '3', NULL),
('2906', '102', 'FISHquant automatic counting of transcripts in 3D FISH images', 'Nat Methods', '10', NULL, '277-8', '2013', '1548-7105', '10.1038/nmeth.2406', '', '2013 Apr', '', '', '4', NULL),
('2915', '102', 'rapidSTORM accurate fast opensource software for localization mi', 'Nat Methods', '9', NULL, '1040-1', '2012', '1548-7105', '10.1038/nmeth.2224', '', '2012 Nov', '', '', '11', NULL),
('2918', '102', 'PixFRET an ImageJ plugin for FRET calculation that can accommoda', 'Microsc Res Tech', '68', NULL, '51-8', '2005', '1059-910X', '10.1002/jemt.20215', '<p>Fluorescence resonance energy transfer (FRET) allows the user to investigate interactions between fluorescent partners. One crucial issue when calculating sensitized emission FRET is the correction for spectral bleed-throughs (SBTs), which requires to calculate the ratios between the intensities in the FRET and in the donor or acceptor settings, when only the donor or acceptor are present. Theoretically, SBT ratios should be constant. However, experimentally, these ratios can vary as a function of fluorophore intensity, and assuming constant values may hinder precise FRET calculation. One possible cause for such a variation is the use of a microscope set-up with different photomultipliers for the donor and FRET channels, a set-up allowing higher speed acquisitions on very dynamic fluorescent molecules in living cells. Herein, we show that the bias introduced by the differential response of the two PMTs can be circumvented by a simple modeling of the SBT ratios as a function of fluorophore intensity. Another important issue when performing FRET is the localization of FRET within the cell or a population of cells. We hence developed a freely available ImageJ plug-in, called PixFRET, that allows a simple and rapid determination of SBT parameters and the display of normalized FRET images. The usefulness of this modeling and of the plug-in are exemplified by the study of FRET in a system where two interacting nuclear receptors labeled with ECFP and EYFP are coexpressed in living cells.</p>', '2005 Sep', '', '', '1', NULL),
('2933', '102', 'TANGO a generic tool for highthroughput 3D image analysis for st', 'Bioinformatics', '29', NULL, '1840-1', '2013', '1367-4811', '10.1093/bioinformatics/btt276', '<p><b>MOTIVATION: </b>The cell nucleus is a highly organized cellular organelle that contains the genetic material. The study of nuclear architecture has become an important field of cellular biology. Extracting quantitative data from 3D fluorescence imaging helps understand the functions of different nuclear compartments. However, such approaches are limited by the requirement for processing and analyzing large sets of images.</p><p><b>RESULTS: </b>Here, we describe Tools for Analysis of Nuclear Genome Organization (TANGO), an image analysis tool dedicated to the study of nuclear architecture. TANGO is a coherent framework allowing biologists to perform the complete analysis process of 3D fluorescence images by combining two environments: ImageJ (http://imagej.nih.gov/ij/) for image processing and quantitative analysis and R (http://cran.r-project.org) for statistical processing of measurement results. It includes an intuitive user interface providing the means to precisely build a segmentation procedure and set-up analyses, without possessing programming skills. TANGO is a versatile tool able to process large sets of images, allowing quantitative study of nuclear organization.</p><p><b>AVAILABILITY: </b>TANGO is composed of two programs: (i) an ImageJ plug-in and (ii) a package (rtango) for R. They are both free and open source, available (http://biophysique.mnhn.fr/tango) for Linux, Microsoft Windows and Macintosh OSX. Distribution is under the GPL v.2 licence.</p><p><b>CONTACT: </b>thomas.boudier@snv.jussieu.fr</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2013 Jul 15', '', '', '14', NULL),
('2956', '102', 'Automated whole animal bioimaging assay for human cancer dissemi', 'PLoS One', '7', NULL, 'e31281', '2012', '1932-6203', '10.1371/journal.pone.0031281', '<p>A quantitative bio-imaging platform is developed for analysis of human cancer dissemination in a short-term vertebrate xenotransplantation assay. Six days after implantation of cancer cells in zebrafish embryos, automated imaging in 96 well plates coupled to image analysis algorithms quantifies spreading throughout the host. Findings in this model correlate with behavior in long-term rodent xenograft models for panels of poorly- versus highly malignant cell lines derived from breast, colorectal, and prostate cancer. In addition, cancer cells with scattered mesenchymal characteristics show higher dissemination capacity than cell types with epithelial appearance. Moreover, RNA interference establishes the metastasis-suppressor role for E-cadherin in this model. This automated quantitative whole animal bio-imaging assay can serve as a first-line in vivo screening step in the anti-cancer drug target discovery pipeline.</p>', '2012', '', '', '2', NULL),
('2960', '102', 'Objective comparison of particle tracking methods', 'Nat Methods', '11', NULL, '281-9', '2014', '1548-7105', '10.1038/nmeth.2808', '<p>Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.</p>', '2014 Mar', '', '', '3', NULL),
('2962', '102', 'Fluorophore localization algorithms for superresolution microsco', 'Nat Methods', '11', NULL, '267-79', '2014', '1548-7105', '10.1038/nmeth.2844', '<p>Super-resolution localization microscopy methods provide powerful new capabilities for probing biology at the nanometer scale via fluorescence. These methods rely on two key innovations: switchable fluorophores (which blink on and off and can be sequentially imaged) and powerful localization algorithms (which estimate the positions of the fluorophores in the images). These techniques have spurred a flurry of innovation in algorithm development over the last several years. In this Review, we survey the fundamental issues for single-fluorophore fitting routines, localization algorithms based on principles other than fitting, three-dimensional imaging, dipole imaging and techniques for estimating fluorophore positions from images of multiple activated fluorophores. We offer practical advice for users and adopters of algorithms, and we identify areas for further development.</p>', '2014 Mar', '', '', '3', NULL),
('2964', '102', 'Software opens the door to quantitative imaging', 'Nat Methods', '4', NULL, '120-1', '2007', '1548-7091', '10.1038/nmeth0207-120', '', '2007 Feb', '', '', '2', NULL),
('2973', '102', 'Fluorescence Lifetime Imaging Microscopy FLIM Data Analysis with', 'Journal of Statistical Software', '18', NULL, '1–20', '2007', '1548-7660', '', 'Fluorescence Lifetime Imaging Microscopy (FLIM) allows fluorescence lifetime images of biological objects to be collected at 250 nm spatial resolution and at (sub-)nanosecond temporal resolution. Often ncomp kinetic processes underlie the observed fluorescence at all locations, but the intensity of the fluorescence associated with each process varies per-location, i.e., per-pixel imaged. Then the statistical challenge is global analysis of the image: use of the fluorescence decay in time at all locations to estimate the ncomp lifetimes associated with the kinetic processes, as well as the amplitude of each kinetic process at each location. Given that typical FLIM images represent on the order of 102 timepoints and 103 locations, meeting this challenge is computationally intensive. Here the utility of the TIMP package for R to solve parameter estimation problems arising in FLIM image analysis is demonstrated. Case studies on simulated and real data evidence the applicability of the partitioned variable projection algorithm implemented in TIMP to the problem domain, and showcase options included in the package for the visual validation of models for FLIM data.', '1', 'http://www.jstatsoft.org/v18/i08/paper', '', '', NULL),
('2983', '102', 'Microtubule dynamics reconstituted in vitro and imaged by single', 'Methods Cell Biol', '95', NULL, '221-45', '2010', '0091-679X', '10.1016/S0091-679X(10)95013-9', '<p>In vitro assays that reconstitute the dynamic behavior of microtubules provide insight into the roles of microtubule-associated proteins (MAPs) in regulating the growth, shrinkage, and catastrophe of microtubules. The use of total internal reflection fluorescence microscopy with fluorescently labeled tubulin and MAPs has allowed us to study microtubule dynamics at the resolution of single molecules. In this chapter we present a practical overview of how these assays are performed in our laboratory: fluorescent labeling methods, strategies to prolong the time to photo-bleaching, preparation of stabilized microtubules, flow-cells, microtubule immobilization, and finally an overview of the workflow that we follow when performing the experiments. At all stages, we focus on practical tips and highlight potential stumbling blocks.</p>', '2010', '', '', '', NULL),
('3000', '102', 'Distinctive Image Features from ScaleInvariant Keypoints', 'International Journal of Computer Vision', '60', NULL, '91-110', '2004', '0920-5691', '10.1023/B:VISI.0000029664.99615.94', '', '', 'http://dx.doi.org/10.1023/B%3AVISI.0000029664.99615.94', '', '', NULL),
('3001', '103', 'Multiimage matching using multiscale oriented patches', 'Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition', 'None', NULL, 'None', '2005', 'None', '', 'This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 &amp;times; 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.', '', '', '', 'None', NULL);
INSERT INTO `academicpaper` (`id_paper`, `id_type`, `Title`, `Journal`, `Volume`, `Number`, `Pages`, `Year`, `ISSN`, `doi`, `Abstract`, `Date`, `URL`, `ISBN`, `Issue`, `Month`) VALUES
('3023', '102', 'Sickle cell anaemia among EtiTurks haematological clinical and g', 'Br J Haematol', '64', NULL, '45-55', '1986', '0007-1048', '', '<p>Haematological and genetic observations have been made on 71 SS Eti-Turk patients and their relatives from Cukurova (southern Turkey) and of immigrant families in The Netherlands. Similar data were collected for 25 Black patients and their relatives from Surinam, Netherlands Antilles, and Kenya. Haematological and clinical results were the same for both groups; the haemolytic anaemia in the Turkish patients was as severe as in the others. Haplotyping, involving nine restriction sites, identified haplotype 19 (Antonarakis et al, 1984) as the major type among the Eti-Turks; this chromosome has previously primarily been observed among SS patients from West Africa. The suggestion that the beta S-chromosome among Eti-Turks originates from that area is supported by a relatively high incidence of alpha-thalassaemia-2 (the 3.7 kb deletion), also frequently present in the Black population of West Africa, and by the absence of other major haplotypes, such as types 20 and 3, characteristic for the beta S-chromosome in the population of Central Africa and Kenya, and in Senegal, respectively. The Saudi Arabian type of beta S chromosome in association with the haplotype 19 beta S chromosome was present in only one Eti-Turk patient; this 30-year-old female was mildly affected and exhibited a high level of fetal haemoglobin.</p>', '1986 Sep', '', '', '1', NULL),
('3026', '102', 'Active mask segmentation of fluorescence microscope images', 'IEEE Trans Image Process', '18', NULL, '1817-29', '2009', '1057-7149', '10.1109/TIP.2009.2021081', '<p>We propose a new active mask algorithm for the segmentation of fluorescence microscope images of punctate patterns. It combines the (a) flexibility offered by active-contour methods, (b) speed offered by multiresolution methods, (c) smoothing offered by multiscale methods, and (d) statistical modeling offered by region-growing methods into a fast and accurate segmentation tool. The framework moves from the idea of the "contour" to that of "inside and outside," or masks, allowing for easy multidimensional segmentation. It adapts to the topology of the image through the use of multiple masks. The algorithm is almost invariant under initialization, allowing for random initialization, and uses a few easily tunable parameters. Experiments show that the active mask algorithm matches the ground truth well and outperforms the algorithm widely used in fluorescence microscopy, seeded watershed, both qualitatively, as well as quantitatively.</p>', '2009 Aug', '', '', '8', NULL),
('3028', '103', 'SLTLoG A Vesicle Segmentation Method with Automatic Scale Select', '{ISBI - 2014 IEEE International Symposium on Biomedical Imaging}', 'None', NULL, 'None', '2014', 'None', '', '', '', 'https://hal.inria.fr/hal-00921793', '', 'None', NULL),
('3031', '102', 'Stepbystep quantitative analysis of focal adhesions', 'MethodsX', '1', NULL, '56–59', '2014', '22150161', '10.1016/j.mex.2014.06.004', '', '', 'http://www.researchgate.net/publication/263737052\\_Step-by-step\\_quantitative\\_analysis\\_of\\_focal\\_adhesions', '', '', NULL),
('3040', '102', 'Endosomal WASH and exocyst complexes control exocytosis of MT1MM', 'J Cell Biol', '203', NULL, '1063-79', '2013', '1540-8140', '10.1083/jcb.201306162', '<p>Remodeling of the extracellular matrix by carcinoma cells during metastatic dissemination requires formation of actin-based protrusions of the plasma membrane called invadopodia, where the trans-membrane type 1 matrix metalloproteinase (MT1-MMP) accumulates. Here, we describe an interaction between the exocyst complex and the endosomal Arp2/3 activator Wiskott-Aldrich syndrome protein and Scar homolog (WASH) on MT1-MMP–containing late endosomes in invasive breast carcinoma cells. We found that WASH and exocyst are required for matrix degradation by an exocytic mechanism that involves tubular connections between MT1-MMP–positive late endosomes and the plasma membrane in contact with the matrix. This ensures focal delivery of MT1-MMP and supports pericellular matrix degradation and tumor cell invasion into different pathologically relevant matrix environments. Our data suggest a general mechanism used by tumor cells to breach the basement membrane and for invasive migration through fibrous collagen-enriched tissues surrounding the tumor.</p>', '2013 Dec 23', '', '', '6', NULL),
('3048', '102', 'Advances in image correlation spectroscopy measuring number dens', 'Cell Biochem Biophys', '49', NULL, '141-64', '2007', '1085-9195', '10.1007/s12013-007-9000-5', '<p>A brief historical outline of fluorescence fluctuation correlation techniques is presented, followed by an in-depth review of the theory and development of image correlation techniques, including: image correlation spectroscopy (ICS), temporal ICS (TICS), image cross-correlation spectroscopy (ICCS), spatiotemporal ICS (STICS), k-space ICS (kICS), raster ICS (RICS), and particle ICS (PICS). These techniques can be applied to analyze image series acquired on commercially available laser scanning or total internal reflection fluorescence microscopes, and are used to determine the number density, aggregation state, diffusion coefficient, velocity, and interaction fraction of fluorescently labeled molecules or particles. A comprehensive review of the application of ICS techniques to a number of systems, including cell adhesion, membrane receptor aggregation and dynamics, virus particle fusion, and fluorophore photophysics, is presented.</p>', '2007', '', '', '3', NULL),
('3084', '102', 'Highthroughput subpixel precision analysis of bacterial morphoge', 'Mol Microbiol', '80', NULL, '612-27', '2011', '1365-2958', '10.1111/j.1365-2958.2011.07579.x', '<p>Bacteria display various shapes and rely on complex spatial organization of their intracellular components for many cellular processes. This organization changes in response to internal and external cues. Quantitative, unbiased study of these spatio-temporal dynamics requires automated image analysis of large microscopy datasets. We have therefore developed MicrobeTracker, a versatile and high-throughput image analysis program that outlines and segments cells with subpixel precision, even in crowded images and mini-colonies, enabling cell lineage tracking. MicrobeTracker comes with an integrated accessory tool, SpotFinder, which precisely tracks foci of fluorescently labelled molecules inside cells. Using MicrobeTracker, we discover that the dynamics of the extensively studied Escherichia coli Min oscillator depends on Min protein concentration, unveiling critical limitations in robustness within the oscillator. We also find that the fraction of MinD proteins oscillating increases with cell length, indicating that the oscillator has evolved to be most effective when cells attain an appropriate length. MicrobeTracker was also used to uncover novel aspects of morphogenesis and cell cycle regulation in Caulobacter crescentus. By tracking filamentous cells, we show that the chromosomal origin at the old-pole is responsible for most replication/separation events while the others remain largely silent despite contiguous cytoplasm. This surprising position-dependent silencing is regulated by division.</p>', '2011 May', '', '', '3', NULL),
('3092', '102', 'High Resolution Tracking of Cell Membrane Dynamics in Moving Cel', 'Mathematical Modelling of Natural Phenomena', '5', NULL, '34–55', '2010', '1760-6101', '10.1051/mmnp/20105102', '', '1', 'http://www.mmnp-journal.org/article_S0973534810051023', '', '', NULL),
('3097', '102', 'FindFoci A Focus Detection Algorithm with Automated Parameter Tr', 'PLoS One', '9', NULL, 'e114749', '2014', '1932-6203', '10.1371/journal.pone.0114749', '<p>Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.</p>', '2014', '', '', '12', NULL),
('3154', '103', 'Curvelet analysis of kymograph for tracking bidirectional partic', 'Image Processing (ICIP), 2010 17th IEEE International Conference on', 'None', NULL, 'None', '2010', 'None', '10.1109/ICIP.2010.5652479', 'In this paper we present a new procedure for tracking bi-directional objects in kymographs. The proposed technique is based on a novel adaptive and directional band-pass filtering method which allows us to separate particles which move in opposite directions. The filtering method exploits the curvelet analysis of the kymograph image to automatically adapt to the objects trails characteristics and select oriented features. The separation of bi-directional objects in separated images allows us to reliably detect and track fluorescent particles in fluorescence image sequences, despite numerous crossroad points in the kymograph space. The new abilities provided by the proposed technique are highlighted by the analysis of biological images which were previously impossible to analyze reliably.', 'Sept', '', '', 'None', NULL),
('3156', '102', 'highlevel 3D visualization API for Java and ImageJ', 'BMC Bioinformatics', '11', NULL, '274', '2010', '1471-2105', '10.1186/1471-2105-11-274', '<p><b>BACKGROUND: </b>Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set.</p><p><b>RESULTS: </b>Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts.</p><p><b>CONCLUSIONS: </b>Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de.</p>', '2010', '', '', '', NULL),
('3167', '102', 'Threedimensional multiscale line filter for segmentation and vis', 'Med Image Anal', '2', NULL, '143-68', '1998', '1361-8415', '', '<p>This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.</p>', '1998 Jun', '', '', '2', NULL),
('3172', '102', 'Automated segmentation and tracking for largescale analysis of f', 'J Microsc', '241', NULL, '37-53', '2011', '1365-2818', '10.1111/j.1365-2818.2010.03404.x', '<p>Cell adhesion, a process mediated by the formation of discrete structures known as focal adhesions (FAs), is pivotal to many biological events including cell motility. Much is known about the molecular composition of FAs, although our knowledge of the spatio-temporal recruitment and the relative occupancy of the individual components present in the FAs is still incomplete. To fill this gap, an essential prerequisite is a highly reliable procedure for the recognition, segmentation and tracking of FAs. Although manual segmentation and tracking may provide some advantages when done by an expert, its performance is usually hampered by subjective judgement and the long time required in analysing large data sets. Here, we developed a model-based segmentation and tracking algorithm that overcomes these problems. In addition, we developed a dedicated computational approach to correct segmentation errors that may arise from the analysis of poorly defined FAs. Thus, by achieving accurate and consistent FA segmentation and tracking, our work establishes the basis for a comprehensive analysis of FA dynamics under various experimental regimes and the future development of mathematical models that simulate FA behaviour.</p>', '2011 Jan', '', '', '1', NULL),
('3183', '102', 'idTracker tracking individuals in a group by automatic identific', 'Nat Methods', '11', NULL, '743-8', '2014', '1548-7105', '10.1038/nmeth.2994', '<p>Animals in groups touch each other, move in paths that cross, and interact in complex ways. Current video tracking methods sometimes switch identities of unmarked individuals during these interactions. These errors propagate and result in random assignments after a few minutes unless manually corrected. We present idTracker, a multitracking algorithm that extracts a characteristic fingerprint from each animal in a video recording of a group. It then uses these fingerprints to identify every individual throughout the video. Tracking by identification prevents propagation of errors, and the correct identities can be maintained indefinitely. idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. We tested it on fish (Danio rerio and Oryzias latipes), flies (Drosophila melanogaster), ants (Messor structor) and mice (Mus musculus).</p>', '2014 Jul', '', '', '7', NULL),
('3208', '102', 'Whole slide quantification of stromal lymphatic vessel distribut', 'ISRN Obstet Gynecol', '2011', NULL, '354861', '2011', '2090-4444', '10.5402/2011/354861', '<p>Peritumoral Lymphatic Vessel Density (LVD) is considered to be a predictive marker for the presence of lymph node metastases in cervical cancer. However, when LVD quantification relies on conventional optical microscopy and the hot spot technique, interobserver variability is significant and yields inconsistent conclusions. In this work, we describe an original method that applies computed image analysis to whole slide scanned tissue sections following immunohistochemical lymphatic vessel staining. This procedure allows to determine an objective LVD quantification as well as the lymphatic vessel distribution and its heterogeneity within the stroma surrounding the invasive tumor bundles. The proposed technique can be useful to better characterize lymphatic vessel interactions with tumor cells and could potentially impact on prognosis and therapeutic decisions.</p>', '2011', '', '', '', NULL),
('3217', '102', 'NonLocal Means Denoising', 'Image Processing On Line', '1', NULL, '', '2011', '', '10.5201/ipol10.5201/ipol.201110.5201/ipol.2011.bcm_nlm', '', 'Jan-01-2011', 'http://www.ipol.im/?utm_source=doihttp://www.ipol.im/pub/art/2011/?utm_source=doihttp://www.ipol.im/pub/art/2011/bcm_nlm/?utm_source=doi', '', '', NULL),
('3218', '102', 'Auxin depletion from leaf primordia contributes to organ pattern', 'Proc Natl Acad Sci U S A', '111', NULL, '18769-74', '2014', '1091-6490', '10.1073/pnas.1421878112', '<p>Stem cells are responsible for organogenesis, but it is largely unknown whether and how information from stem cells acts to direct organ patterning after organ primordia are formed. It has long been proposed that the stem cells at the plant shoot apex produce a signal, which promotes leaf adaxial-abaxial (dorsoventral) patterning. Here we show the existence of a transient low auxin zone in the adaxial domain of early leaf primordia. We also demonstrate that this adaxial low auxin domain contributes to leaf adaxial-abaxial patterning. The auxin signal is mediated by the auxin-responsive transcription factor MONOPTEROS (MP), whose constitutive activation in the adaxial domain promotes abaxial cell fate. Furthermore, we show that auxin flow from emerging leaf primordia to the shoot apical meristem establishes the low auxin zone, and that this auxin flow contributes to leaf polarity. Our results provide an explanation for the hypothetical meristem-derived leaf polarity signal. Opposite to the original proposal, instead of a signal derived from the meristem, we show that a signaling molecule is departing from the primordium to the meristem to promote robustness in leaf patterning.</p>', '2014 Dec 30', '', '', '52', NULL),
('3220', '102', 'unbiased detector of curvilinear structures', 'IEEE Transactions on Pattern Analysis and Machine Intelligence', '20', NULL, '113 - 125', '1998', '01628828', '10.1109/34.659930', '', 'Jan-01-1970', 'http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=659930', '', '2', NULL),
('3223', '102', 'NIH Image to ImageJ 25 years of image analysis', 'Nature Methods', '9', NULL, '671 - 675', '2012', '1548-7091', '10.1038/nmeth.2089', '', 'Apr-06-2014', 'http://www.nature.com/doifinder/10.1038/nmeth.2089', '', '7', NULL),
('3239', '102', 'OpenComet an automated tool for comet assay image analysis', 'Redox Biol', '2', NULL, '457-65', '2014', '2213-2317', '10.1016/j.redox.2013.12.020', '<p>Reactive species such as free radicals are constantly generated in vivo and DNA is the most important target of oxidative stress. Oxidative DNA damage is used as a predictive biomarker to monitor the risk of development of many diseases. The comet assay is widely used for measuring oxidative DNA damage at a single cell level. The analysis of comet assay output images, however, poses considerable challenges. Commercial software is costly and restrictive, while free software generally requires laborious manual tagging of cells. This paper presents OpenComet, an open-source software tool providing automated analysis of comet assay images. It uses a novel and robust method for finding comets based on geometric shape attributes and segmenting the comet heads through image intensity profile analysis. Due to automation, OpenComet is more accurate, less prone to human bias, and faster than manual analysis. A live analysis functionality also allows users to analyze images captured directly from a microscope. We have validated OpenComet on both alkaline and neutral comet assay images as well as sample images from existing software packages. Our results show that OpenComet achieves high accuracy with significantly reduced analysis time.</p>', '2014', '', '', '', NULL),
('3257', '102', 'selforganized biomechanical network drives shape changes during ', 'Nature', '524', NULL, '351-5', '2015', '1476-4687', '10.1038/nature14603', '<p>Tissue morphogenesis is orchestrated by cell shape changes. Forces required to power these changes are generated by non-muscle myosin II (MyoII) motor proteins pulling filamentous actin (F-actin). Actomyosin networks undergo cycles of assembly and disassembly (pulses) to cause cell deformations alternating with steps of stabilization to result in irreversible shape changes. Although this ratchet-like behaviour operates in a variety of contexts, the underlying mechanisms remain unclear. Here we investigate the role of MyoII regulation through the conserved Rho1-Rok pathway during Drosophila melanogaster germband extension. This morphogenetic process is powered by cell intercalation, which involves the shrinkage of junctions in the dorsal-ventral axis (vertical junctions) followed by junction extension in the anterior-posterior axis. While polarized flows of medial-apical MyoII pulses deform vertical junctions, MyoII enrichment on these junctions (planar polarity) stabilizes them. We identify two critical properties of MyoII dynamics that underlie stability and pulsatility: exchange kinetics governed by phosphorylation-dephosphorylation cycles of the MyoII regulatory light chain; and advection due to contraction of the motors on F-actin networks. Spatial control over MyoII exchange kinetics establishes two stable regimes of high and low dissociation rates, resulting in MyoII planar polarity. Pulsatility emerges at intermediate dissociation rates, enabling convergent advection of MyoII and its upstream regulators Rho1 GTP, Rok and MyoII phosphatase. Notably, pulsatility is not an outcome of an upstream Rho1 pacemaker. Rather, it is a self-organized system that involves positive and negative biomechanical feedback between MyoII advection and dissociation rates.</p>', '2015 Aug 20', '', '', '7565', NULL),
('3258', '102', 'CellTrack an opensource software for cell tracking and motility ', 'Bioinformatics', '24', NULL, '1647 - 1649', '2008', '1367-4803', '10.1093/bioinformatics/btn247', '', 'Mar-07-2009', 'http://bioinformatics.oxfordjournals.org/cgi/doi/10.1093/bioinformatics/btn247', '', '14', NULL),
('3260', '102', 'Phase locking and multiple oscillating attractors for the couple', 'Proceedings of the National Academy of Sciences', '111', NULL, '9828 - 9833', '2014', '0027-8424', '10.1073/pnas.1320474111', '', 'Aug-07-2014', 'http://www.pnas.org/cgi/doi/10.1073/pnas.1320474111', '', '27', NULL),
('3261', '102', 'Extracting Fluorescent Reporter Time Courses of Cell Lineages fr', 'PLoS ONE', '6', NULL, 'e27886', '2011', '', '10.1371/journal.pone.0027886', '', 'Mar-12-2012', 'http://dx.plos.org/10.1371/journal.pone.0027886', '', '12', NULL),
('3276', '102', 'FibrilTool an ImageJ plugin to quantify fibrillar structures in ', 'Nat Protoc', '9', NULL, '457-63', '2014', '1750-2799', '10.1038/nprot.2014.024', '<p>Cell biology heavily relies on the behavior of fibrillar structures, such as the cytoskeleton, yet the analysis of their behavior in tissues often remains qualitative. Image analysis tools have been developed to quantify this behavior, but they often involve an image pre-processing stage that may bias the output and/or they require specific software. Here we describe FibrilTool, an ImageJ plug-in based on the concept of nematic tensor, which can provide a quantitative description of the anisotropy of fiber arrays and their average orientation in cells, directly from raw images obtained by any form of microscopy. FibrilTool has been validated on microtubules, actin and cellulose microfibrils, but it may also help analyze other fibrillar structures, such as collagen, or the texture of various materials. The tool is ImageJ-based, and it is therefore freely accessible to the scientific community and does not require specific computational setup. The tool provides the average orientation and anisotropy of fiber arrays in a given region of interest (ROI) in a few seconds.</p>', '2014 Feb', '', '', '2', NULL),
('3281', '102', 'Statistical analysis of 3D images detects regular spatial distri', 'PLoS Comput Biol', '6', NULL, 'e1000853', '2010', '1553-7358', '10.1371/journal.pcbi.1000853', '<p>In eukaryotes, the interphase nucleus is organized in morphologically and/or functionally distinct nuclear "compartments". Numerous studies highlight functional relationships between the spatial organization of the nucleus and gene regulation. This raises the question of whether nuclear organization principles exist and, if so, whether they are identical in the animal and plant kingdoms. We addressed this issue through the investigation of the three-dimensional distribution of the centromeres and chromocenters. We investigated five very diverse populations of interphase nuclei at different differentiation stages in their physiological environment, belonging to rabbit embryos at the 8-cell and blastocyst stages, differentiated rabbit mammary epithelial cells during lactation, and differentiated cells of Arabidopsis thaliana plantlets. We developed new tools based on the processing of confocal images and a new statistical approach based on G- and F- distance functions used in spatial statistics. Our original computational scheme takes into account both size and shape variability by comparing, for each nucleus, the observed distribution against a reference distribution estimated by Monte-Carlo sampling over the same nucleus. This implicit normalization allowed similar data processing and extraction of rules in the five differentiated nuclei populations of the three studied biological systems, despite differences in chromosome number, genome organization and heterochromatin content. We showed that centromeres/chromocenters form significantly more regularly spaced patterns than expected under a completely random situation, suggesting that repulsive constraints or spatial inhomogeneities underlay the spatial organization of heterochromatic compartments. The proposed technique should be useful for identifying further spatial features in a wide range of cell types.</p>', '2010', '', '', '7', NULL),
('3285', '102', '3D reconstruction of histological sections Application to mammar', 'Microscopy Research and Technique', '73', NULL, '1019 - 1029', '2010', '', '10.1002/jemt.v73:1110.1002/jemt.20829', '', 'Jan-10-2010', 'http://doi.wiley.com/10.1002/jemt.v73%3A11http://doi.wiley.com/10.1002/jemt.20829http://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fjemt.20829', '', '11', NULL),
('3343', '102', 'Measuring image resolution in optical nanoscopy', 'Nat Methods', '10', NULL, '557-62', '2013', '1548-7105', '10.1038/nmeth.2448', '<p>Resolution in optical nanoscopy (or super-resolution microscopy) depends on the localization uncertainty and density of single fluorescent labels and on the sample\'s spatial structure. Currently there is no integral, practical resolution measure that accounts for all factors. We introduce a measure based on Fourier ring correlation (FRC) that can be computed directly from an image. We demonstrate its validity and benefits on two-dimensional (2D) and 3D localization microscopy images of tubulin and actin filaments. Our FRC resolution method makes it possible to compare achieved resolutions in images taken with different nanoscopy methods, to optimize and rank different emitter localization and labeling strategies, to define a stopping criterion for data acquisition, to describe image anisotropy and heterogeneity, and even to estimate the average number of localizations per emitter. Our findings challenge the current focus on obtaining the best localization precision, showing instead how the best image resolution can be achieved as fast as possible.</p>', '2013 Jun', '', '', '6', NULL),
('3344', '102', 'Fourier shell correlation threshold criteria', 'J Struct Biol', '151', NULL, '250-62', '2005', '1047-8477', '10.1016/j.jsb.2005.05.009', '<p>The resolution value claimed for an electron microscopical three-dimensional reconstruction indicates the overall quality of the experiment. The Fourier shell correlation (FSC) criterion has now become the standard quality measure. However, what has continued to be controversial is the issue of the FSC threshold level at which one defines the reproducible resolution. Here, we discuss the theoretical behaviour of the FSC in conjunction with the various factors which influence it: the number of "voxels" in a given Fourier shell, the symmetry of the structure, and the size of the structure within the reconstruction volume. Both the theoretical considerations and our model experiments show that fixed-valued FSC threshold (like "0.5") may never be used in a reproducible criterion. Fixed threshold values are-as we show here-simply the result of incorrect assumptions in the basic statistics. Two families of FSC threshold curves are discussed: the sigma-factor curves and the new family of bit-based information threshold curves. Whereas sigma-factor curves indicate the resolution level at which one has collected information significantly above the noise level, the information curves indicate the resolution level at which enough information has been collected for interpretation.</p>', '2005 Sep', '', '', '3', NULL),
('3345', '102', 'MorphoLibJ integrated library and plugins for mathematical morph', 'Bioinformatics', '', NULL, '', '2016', '1367-4811', '10.1093/bioinformatics/btw413', '<p><b>MOTIVATION: </b>Mathematical morphology (MM) provides many powerful operators for processing 2D and 3D images. However, most MM plugins currently implemented for the popular ImageJ/Fiji platform are limited to the processing of 2D images.</p><p><b>RESULTS: </b>The MorphoLibJ library proposes a large collection of generic tools based on MM to process binary and grey-level 2D and 3D images, integrated into user-friendly plugins. We illustrate how MorphoLibJ can facilitate the exploitation of 3D images of plant tissues.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>MorphoLibJ is freely available at http://imagej.net/MorphoLibJ CONTACT: david.legland@nantes.inra.frSupplementary information: Supplementary data are available at Bioinformatics online.</p>', '2016 Jul 13', '', '', '', NULL),
('3349', '102', 'neuTube 10 A New Design for Efficient Neuron Reconstruction Soft', 'eNeuro', '2', NULL, '', '2015', '', '10.1523/ENEURO.0049-14.2014', '', '', '', '', '1', NULL),
('3357', '102', 'SRTesseler a method to segment and quantify localizationbased su', 'Nature Methods', '12', NULL, '', '2015', '', '10.1038/nmeth.3579', '', '', '', '', '', NULL),
('3358', '102', 'Opensource image reconstruction of superresolution structured il', 'Nat Commun', '7', NULL, '10980', '2016', '2041-1723', '10.1038/ncomms10980', '<p>Super-resolved structured illumination microscopy (SR-SIM) is an important tool for fluorescence microscopy. SR-SIM microscopes perform multiple image acquisitions with varying illumination patterns, and reconstruct them to a super-resolved image. In its most frequent, linear implementation, SR-SIM doubles the spatial resolution. The reconstruction is performed numerically on the acquired wide-field image data, and thus relies on a software implementation of specific SR-SIM image reconstruction algorithms. We present fairSIM, an easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.</p>', '2016', '', '', '', NULL),
('3364', '102', 'SIMToolbox a MATLAB toolbox for structured illumination fluoresc', 'Bioinformatics', '32', NULL, '318-20', '2016', '1367-4811', '10.1093/bioinformatics/btv576', '<p><b>UNLABELLED: </b>SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>SIMToolbox, example data and the online documentation are freely accessible at http://mmtg.fel.cvut.cz/SIMToolbox.</p><p><b>CONTACT: </b>ghagen@uccs.edu</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2016 Jan 15', '', '', '2', NULL),
('3406', '102', 'Parallel DistributedMemory Particle Method Enables AcquisitionRa', 'PLOS One', '11', NULL, 'e0152528', '2016', '', '10.1371/journal.pone.0152528', '', '', '', '', '4', NULL),
('3411', '102', 'ClusDoC A combined cluster detection and colocalization analysis', 'Mol Biol Cell', '', NULL, '', '2016', '1939-4586', '10.1091/mbc.E16-07-0478', '<p>Advances in fluorescence microscopy are providing increasing evidence that the spatial organization of proteins in cell membranes may facilitate signal initiation and integration for appropriate cellular responses. Our understanding of how changes in spatial organization are linked to function has been hampered by the inability to directly measure signaling activity or protein association at the level of individual proteins in intact cells. Here, we have solved this measurement challenge by developing Clus-DoC, an analysis strategy that quantifies both the spatial distribution of a protein as well as its colocalization status. We applied this approach to the triggering of the T-cell receptor (TCR) during T-cell activation as well as to the functionality of focal adhesions in fibroblasts, thereby demonstrating an experimental and analytical workflow that may be used to quantify signaling activity and protein colocalization at the level of individual proteins.</p>', '2016 Aug 31', '', '', '', NULL),
('3413', '102', 'Realtime analysis and visualization for singlemolecule based sup', 'PLoS One', '8', NULL, 'e62918', '2013', '1932-6203', '10.1371/journal.pone.0062918', '<p>Accurate multidimensional localization of isolated fluorescent emitters is a time consuming process in single-molecule based super-resolution microscopy. We demonstrate a functional method for real-time reconstruction with automatic feedback control, without compromising the localization accuracy. Compatible with high frame rates of EM-CCD cameras, it relies on a wavelet segmentation algorithm, together with a mix of CPU/GPU implementation. A combination with Gaussian fitting allows direct access to 3D localization. Automatic feedback control ensures optimal molecule density throughout the acquisition process. With this method, we significantly improve the efficiency and feasibility of localization-based super-resolution microscopy.</p>', '2013', '', '', '4', NULL),
('3415', '102', 'software framework for the analysis of complex microscopy image ', 'IEEE Trans Inf Technol Biomed', '14', NULL, '1075-87', '2010', '1558-0032', '10.1109/TITB.2010.2049024', '<p>Technological advances in both hardware and software have made possible the realization of sophisticated biological imaging experiments using the optical microscope. As a result, modern microscopy experiments are capable of producing complex image datasets. For a given data analysis task, the images in a set are arranged, based on the requirements of the task, by attributes such as the time and focus levels at which they were acquired. Importantly, different tasks performed over the course of an analysis are often facilitated by the use of different arrangements of the images. We present a software framework that supports the use of different logical image arrangements to analyze a physical set of images. This framework, called the Microscopy Image Analysis Tool (MIATool), realizes the logical arrangements using arrays of pointers to the images, thereby removing the need to replicate and manipulate the actual images in their storage medium. In order that they may be tailored to the specific requirements of disparate analysis tasks, these logical arrangements may differ in size and dimensionality, with no restrictions placed on the number of dimensions and the meaning of each dimension. MIATool additionally supports processing flexibility, extensible image processing capabilities, and data storage management.</p>', '2010 Jul', '', '', '4', NULL),
('3419', '102', 'Fast Optimized Cluster Algorithm for Localizations FOCAL a spati', 'Bioinformatics', '32', NULL, '747-54', '2016', '1367-4811', '10.1093/bioinformatics/btv630', '<p><b>MOTIVATION: </b>Single-molecule localization microscopy (SMLM) microscopy provides images of cellular structure at a resolution an order of magnitude below what can be achieved by conventional diffraction limited techniques. The concomitantly larger data sets generated by SMLM require increasingly efficient image analysis software. Density based clustering algorithms, with the most ubiquitous being DBSCAN, are commonly used to quantitatively assess sub-cellular assemblies. DBSCAN, however, is slow, scaling with the number of localizations like O(n log (n)) at best, and it\'s performance is highly dependent upon a subjectively selected choice of parameters.</p><p><b>RESULTS: </b>We have developed a grid-based clustering algorithm FOCAL, which explicitly accounts for several dominant artifacts arising in SMLM image reconstructions. FOCAL is fast and efficient, scaling like O(n), and only has one set parameter. We assess DBSCAN and FOCAL on experimental dSTORM data of clusters of eukaryotic RNAP II and PALM data of the bacterial protein H-NS, then provide a detailed comparison via simulation. FOCAL performs comparable and often superior to DBSCAN while yielding a significantly faster analysis. Additionally, FOCAL provides a novel method for filtering out of focus clusters from complex SMLM images.</p><p><b>AVAILABILITY AND IMPLEMENTATION: </b>The data and code are available at: http://www.utm.utoronto.ca/milsteinlab/resources/Software/FOCAL/ CONTACT: josh.milstein@utoronto.ca</p><p><b>SUPPLEMENTARY INFORMATION: </b>Supplementary data are available at Bioinformatics online.</p>', '2016 Mar 1', '', '', '5', NULL),
('3421', '102', 'guided tour into subcellular colocalization analysis in light mi', 'J Microsc', '224', NULL, '213-32', '2006', '0022-2720', '10.1111/j.1365-2818.2006.01706.x', '<p>It is generally accepted that the functional compartmentalization of eukaryotic cells is reflected by the differential occurrence of proteins in their compartments. The location and physiological function of a protein are closely related; local information of a protein is thus crucial to understanding its role in biological processes. The visualization of proteins residing on intracellular structures by fluorescence microscopy has become a routine approach in cell biology and is increasingly used to assess their colocalization with well-characterized markers. However, image-analysis methods for colocalization studies are a field of contention and enigma. We have therefore undertaken to review the most currently used colocalization analysis methods, introducing the basic optical concepts important for image acquisition and subsequent analysis. We provide a summary of practical tips for image acquisition and treatment that should precede proper colocalization analysis. Furthermore, we discuss the application and feasibility of colocalization tools for various biological colocalization situations and discuss their respective strengths and weaknesses. We have created a novel toolbox for subcellular colocalization analysis under ImageJ, named JACoP, that integrates current global statistic methods and a novel object-based approach.</p>', '2006 Dec', '', '', 'Pt 3', NULL),
('3425', '102', 'Intravital placenta imaging reveals microcirculatory dynamics im', 'PLoS Pathog', '9', NULL, 'e1003154', '2013', '1553-7374', '10.1371/journal.ppat.1003154', '<p>Malaria in pregnancy is exquisitely aggressive, causing a range of adverse maternal and fetal outcomes prominently linked to Plasmodium-infected erythrocyte cytoadherence to fetal trophoblast. To elucidate the physiopathology of infected erythrocytes (IE) sequestration in the placenta we devised an experimental system for intravital placental examination of P. berghei-infected mice. BALB/c females were mated to C57Bl/6 CFP+ male mice and infected with GFP+ P. berghei IE, and at gestational day 18, placentas were exposed for time-lapse imaging acquisition under two-photon microscopy. Real-time images and quantitative measurements revealed that trophoblast conformational changes transiently restrain blood flow in the mouse placental labyrinth. The complex dynamics of placental microcirculation promotes IE accumulation in maternal blood spaces with low blood flow and allows the establishment of stable IE-trophoblast contacts. Further, we show that the fate of sequestered IE includes engulfment by both macrophagic and trophoblastic fetal-derived cells. These findings reinforce the current paradigm that IE interact with the trophoblast and provide definitive evidence on two novel pathogenesis mechanisms: (1) trophoblast layer controls placental microcirculation promoting IE sequestration; and (2) fetal-derived placental cells engulf sequestered IE.</p>', '2013 Jan', '', '', '1', NULL),
('3428', '102', 'SynPAnal software for rapid quantification of the density and in', 'PLoS One', '9', NULL, 'e115298', '2014', '1932-6203', '10.1371/journal.pone.0115298', '<p>Continuous modification of the protein composition at synapses is a driving force for the plastic changes of synaptic strength, and provides the fundamental molecular mechanism of synaptic plasticity and information storage in the brain. Studying synaptic protein turnover is not only important for understanding learning and memory, but also has direct implication for understanding pathological conditions like aging, neurodegenerative diseases, and psychiatric disorders. Proteins involved in synaptic transmission and synaptic plasticity are typically concentrated at synapses of neurons and thus appear as puncta (clusters) in immunofluorescence microscopy images. Quantitative measurement of the changes in puncta density, intensity, and sizes of specific proteins provide valuable information on their function in synaptic transmission, circuit development, synaptic plasticity, and synaptopathy. Unfortunately, puncta quantification is very labor intensive and time consuming. In this article, we describe a software tool designed for the rapid semi-automatic detection and quantification of synaptic protein puncta from 2D immunofluorescence images generated by confocal laser scanning microscopy. The software, dubbed as SynPAnal (for Synaptic Puncta Analysis), streamlines data quantification for puncta density and average intensity, thereby increases data analysis throughput compared to a manual method. SynPAnal is stand-alone software written using the JAVA programming language, and thus is portable and platform-free.</p>', '2014', '', '', '12', NULL),
('3430', '102', 'Automatic Bayesian single molecule identification for localizati', 'Sci Rep', '6', NULL, '33521', '2016', '2045-2322', '10.1038/srep33521', '<p>Single molecule localization microscopy (SMLM) is on its way to become a mainstream imaging technique in the life sciences. However, analysis of SMLM data is biased by user provided subjective parameters required by the analysis software. To remove this human bias we introduce here the Auto-Bayes method that executes the analysis of SMLM data automatically. We demonstrate the success of the method using the photoelectron count of an emitter as selection characteristic. Moreover, the principle can be used for any characteristic that is bimodally distributed with respect to false and true emitters. The method also allows generation of an emitter reliability map for estimating quality of SMLM-based structures. The potential of the Auto-Bayes method is shown by the fact that our first basic implementation was able to outperform all software packages that were compared in the ISBI online challenge in 2015, with respect to molecule detection (Jaccard index).</p>', '2016', '', '', '', NULL),
('3435', '102', 'ViBEZ a framework for 3D virtual colocalization analysis in zebr', 'Nature Methods', '9', NULL, '', '2012', '', '10.1038/nmeth.2076', '', '', 'http://www.nature.com/nmeth/journal/v9/n7/pdf/nmeth.2076.pdf', '', '7', NULL),
('3456', '102', 'Infection Counter Automated Quantification of in Vitro Virus Rep', 'Viruses', '8', NULL, '', '2016', '1999-4915', '10.3390/v8070201', '<p>The ability to accurately and reliably quantify viral infection is essential to basic and translational virology research. Here, we describe a simple and robust automated method for using fluorescence microscopy to estimate the proportion of virally infected cells in a monolayer. We provide details of the automated analysis workflow along with a freely available open-source ImageJ plugin, Infection Counter, for performing image quantification. Using hepatitis C virus (HCV) as an example, we have experimentally verified our method, demonstrating that it is equivalent, if not better, than the established focus-forming assay. Finally, we used Infection Counter to assess the anti-HCV activity of SMBz-CsA, a non-immunosuppressive cyclosporine analogue.</p>', '2016', '', '', '7', NULL),
('3475', '102', 'jicbioimage a tool for automated and reproducible bioimage analy', 'PeerJ', '4', NULL, 'e2674', '2016', '2167-8359', '10.7717/peerj.2674', '', '', 'https://peerj.com/articles/2674', '', '', NULL);

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-- --------------------------------------------------------

--
-- Structure de la table `authors`
--

CREATE TABLE `authors` (
  `id_author` decimal(10,0) NOT NULL DEFAULT '0',
  `Complete_name` varchar(255) DEFAULT NULL,
  `Affiliation` longtext
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `authors`
--

INSERT INTO `authors` (`id_author`, `Complete_name`, `Affiliation`) VALUES
('1', 'Cordelières, Fabrice P', ''),
('2', 'Bolte, Susanne', ''),
('3', 'Schneider, Caroline A', ''),
('4', 'Rasband, Wayne S', ''),
('5', 'Eliceiri, Kevin W', ''),
('6', 'Sun, Yu', ''),
('7', 'Duthaler, Stefan', ''),
('8', 'Nelson, Bradley J', ''),
('9', 'Helmuth, Jo A', ''),
('10', 'Paul, Grégory', ''),
('11', 'Sbalzarini, Ivo F', ''),
('12', 'Mayer, Christian', ''),
('13', 'Dimopoulos, Sotiris', ''),
('14', 'Rudolf, Fabian', ''),
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('16', 'Cromey, Douglas W', ''),
('17', 'Wen-Hsiang Tsai', ''),
('18', 'Ponti, Aaron', ''),
('19', 'Schwarb, Patrick', ''),
('20', 'Gulati, Asheesh', ''),
('21', 'Bäcker, Volker', ''),
('22', 'Ruifrok, A C', ''),
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('24', 'Tuominen, Vilppu J', ''),
('25', 'Ruotoistenmäki, Sanna', ''),
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('27', 'Jumppanen, Mervi', ''),
('28', 'Isola, Jorma', ''),
('29', 'Berthold, Michael R', ''),
('30', 'Goldberg, Ilya G', ''),
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('32', 'Manjunath, B S', ''),
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('35', 'Peng, Hanchuan', ''),
('36', 'Plant, Anne L', ''),
('37', 'Roysam, Badrinath', ''),
('38', 'Stuurman, Nico', ''),
('39', 'Stuurmann, Nico', ''),
('40', 'Swedlow, Jason R', ''),
('41', 'Tomancak, Pavel', ''),
('42', 'Carpenter, Anne E', ''),
('43', 'Broders-Bondon, Florence', ''),
('44', 'Paul-Gilloteaux, Perrine', ''),
('45', 'Carlier, Camille', ''),
('46', 'Radice, Glenn L', ''),
('47', 'Dufour, Sylvie', ''),
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('51', 'Zima, Aleksey V', ''),
('52', 'Blatter, Lothar A', ''),
('53', 'Bers, Donald M', ''),
('54', 'Cheng, H', ''),
('55', 'Song, L S', ''),
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('59', 'Ríos, E', ''),
('60', 'Stern, M D', ''),
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('62', 'Lievens, Eva J P', ''),
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('77', 'Zinck, E', ''),
('78', 'Tolonen, Teemu T', ''),
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('84', 'Schmid, Sandra L', ''),
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('86', 'Petit, Valérie', ''),
('87', 'Kumasaka, Mayuko', ''),
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('90', 'Gallagher, Stuart J', ''),
('91', 'Larue, Lionel', ''),
('92', 'Macenko, M.', ''),
('93', 'Niethammer, M.', ''),
('94', 'Marron, J.S.', ''),
('95', 'Borland, D.', ''),
('96', 'Woosley, J.T.', ''),
('97', 'Xiaojun Guan', ''),
('98', 'Schmitt, C.', ''),
('99', 'Thomas, N.E.', ''),
('100', 'Parton, Richard M', ''),
('101', 'Hamilton, Russell S', ''),
('102', 'Ball, Graeme', ''),
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('104', 'Cullen, C Fiona', ''),
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('112', 'Carine Munaut', ''),
('113', 'Frédéric Goffin', ''),
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('117', 'Pengo, Thomas', ''),
('118', 'Holden, Seamus J', ''),
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('128', 'Bouteillé, Marie', ''),
('129', 'Stitt, Mark', ''),
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('131', 'Muller, Bertrand', ''),
('132', 'Zanella, R', ''),
('133', 'Zanghirati, G', ''),
('134', 'Cavicchioli, R', ''),
('135', 'Zanni, L', ''),
('136', 'Boccacci, P', ''),
('137', 'Bertero, M', ''),
('138', 'Vicidomini, G', ''),
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('143', 'Niemann, Axel', ''),
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('146', 'Fernández, José A', ''),
('147', 'Bankhead, Peter', ''),
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('149', 'McGeown, J Graham', ''),
('150', 'Curtis, Tim M', ''),
('151', 'Mueller, Florian', ''),
('152', 'Senecal, Adrien', ''),
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('154', 'Marie-Nelly, Hervé', ''),
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('163', 'Holm, Thorge', ''),
('164', 'Aufmkolk, Sarah', ''),
('165', 'Dabauvalle, Marie-Christine', ''),
('166', 'van de Linde, Sebastian', ''),
('167', 'Sauer, Markus', ''),
('168', 'Feige, Jérôme N', ''),
('169', 'Sage, Daniel', ''),
('170', 'Wahli, Walter', ''),
('171', 'Desvergne, Béatrice', ''),
('172', 'Gelman, Laurent', ''),
('173', 'Ollion, Jean', ''),
('174', 'Cochennec, Julien', ''),
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('176', 'Escudé, Christophe', ''),
('177', 'Boudier, Thomas', ''),
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('179', 'Vicente-Page, Julián', ''),
('180', 'Hinz, Robert C', ''),
('181', 'Arganda, Sara', ''),
('182', 'de Polavieja, Gonzalo G', ''),
('183', 'Ghotra, Veerander P S', ''),
('184', 'He, Shuning', ''),
('185', 'de Bont, Hans', ''),
('186', 'van der Ent, Wietske', ''),
('187', 'Spaink, Herman P', ''),
('188', 'van de Water, Bob', ''),
('189', 'Snaar-Jagalska, B Ewa', ''),
('190', 'Danen, Erik H J', ''),
('191', 'Chenouard, Nicolas', ''),
('192', 'Smal, Ihor', ''),
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('194', 'Maška, Martin', ''),
('195', 'Gong, Yuanhao', ''),
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('198', 'Coraluppi, Stefano', ''),
('199', 'Winter, Mark', ''),
('200', 'Cohen, Andrew R', ''),
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('205', 'Duncan, James', ''),
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('2163', NULL, '0', NULL, 'Mahotas / Thresholding', NULL, NULL, NULL, 'http://mahotas.readthedocs.org/en/latest/thresholding.html', NULL),
('2164', NULL, '0', NULL, 'Mahotas / Wavelets', NULL, NULL, NULL, 'http://mahotas.readthedocs.org/en/latest/wavelets.html', NULL),
('2165', NULL, '0', NULL, 'MembraneQuant', NULL, NULL, NULL, 'http://www.3dhistech.com/membranequant_module ', NULL),
('2167', NULL, '0', NULL, 'NuclearQuant', NULL, NULL, NULL, 'http://www.3dhistech.com/nuclearquant_module ', NULL),
('2168', NULL, '0', NULL, 'OMERO.editor', NULL, NULL, NULL, 'https://www.openmicroscopy.org/site/support/omero5/users/client-tutorials/editor/editor.html', NULL),
('2169', NULL, '0', NULL, 'OMERO.insight client', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/omero5/users/clients-overview.html', NULL),
('2170', NULL, '0', NULL, 'OMERO.insight-ij client', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/omero5/users/client-tutorials/imagej.html', NULL),
('2171', NULL, '0', NULL, 'OMERO.matlab', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/omero5/developers/Matlab.html', NULL),
('2172', NULL, '0', NULL, 'OMERO.searcher', NULL, NULL, NULL, 'http://murphylab.web.cmu.edu/software/searcher/', NULL),
('2173', NULL, '0', NULL, 'OMERO.server', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/omero5/sysadmins/server-overview.html', NULL),
('2174', NULL, '0', NULL, 'OMERO.web client', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/omero5/users/clients-overview.html', NULL),
('2175', NULL, '0', NULL, 'OMERO.webtagging', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site/support/partner/omero.webtagging', NULL),
('2176', NULL, '0', NULL, 'Open Microscopy Environment', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site', NULL),
('2177', NULL, '0', NULL, 'OpenCV / 2D Features', NULL, NULL, NULL, 'opencv.org', NULL),
('2178', NULL, '0', NULL, 'OpenCV / Biologically Inspired Vision Models and Derivative Tools', NULL, NULL, NULL, 'opencv.org', NULL),
('2179', NULL, '0', NULL, 'OpenCV / CUDA', NULL, NULL, NULL, 'opencv.org', NULL),
('2180', NULL, '0', NULL, 'OpenCV / Camera Calibration', NULL, NULL, NULL, 'opencv.org', NULL),
('2181', NULL, '0', NULL, 'OpenCV / Computational Photography', NULL, NULL, NULL, 'opencv.org', NULL),
('2182', NULL, '0', NULL, 'OpenCV / FLANN. Clustering and Search in Multi-Dimensional Spaces', NULL, NULL, NULL, 'opencv.org', NULL),
('2183', NULL, '0', NULL, 'OpenCV / Generic Numerical Optimization', NULL, NULL, NULL, 'opencv.org', NULL),
('2184', NULL, '0', NULL, 'OpenCV / HighGUI', NULL, NULL, NULL, 'opencv.org', NULL),
('2185', NULL, '0', NULL, 'OpenCV / Image Processing', NULL, NULL, NULL, 'opencv.org', NULL),
('2186', NULL, '0', NULL, 'OpenCV / Image Stiching', NULL, NULL, NULL, 'opencv.org', NULL),
('2187', NULL, '0', NULL, 'OpenCV / Machine Learning', NULL, NULL, NULL, 'opencv.org', NULL),
('2188', NULL, '0', NULL, 'OpenCV / Object Detection', NULL, NULL, NULL, 'opencv.org', NULL),
('2189', NULL, '0', NULL, 'OpenCV / OpenCL ', NULL, NULL, NULL, 'opencv.org', NULL),
('2190', NULL, '0', NULL, 'OpenCV / Shape Distance and Matching', NULL, NULL, NULL, 'opencv.org', NULL),
('2191', NULL, '0', NULL, 'OpenCV / Soft Cascade Object Detection and Training', NULL, NULL, NULL, 'opencv.org', NULL),
('2192', NULL, '0', NULL, 'OpenCV / Video Processing', NULL, NULL, NULL, 'opencv.org', NULL),
('2193', NULL, '0', NULL, 'PSF Tool 2D', NULL, NULL, NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2194', NULL, '0', NULL, 'PSF Tool 3D (beta)', NULL, NULL, NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2195', NULL, '0', NULL, 'Panoramic Viewer', NULL, NULL, NULL, 'http://www.3dhistech.com/pannoramic_viewer ', NULL),
('2197', NULL, '0', NULL, 'PhagoTracker', NULL, NULL, NULL, 'http://www.weizmann.ac.il/vet/IC/software/wis-phagotracker ', NULL),
('2198', NULL, '0', NULL, 'PhenoRipper', NULL, NULL, NULL, 'http://www4.utsouthwestern.edu/altschulerwulab/phenoripper/', NULL),
('2199', NULL, '0', NULL, 'Region Competition', NULL, NULL, NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2202', NULL, '0', NULL, 'TurtleSeg', NULL, NULL, NULL, 'http://www.turtleseg.org', NULL),
('2204', NULL, '0', NULL, 'Volocity Acquisition', NULL, NULL, NULL, 'http://www.perkinelmer.com/pages/020/cellularimaging/products/volocityacquisition.xhtml ', NULL),
('2205', NULL, '0', NULL, 'Volocity Restoration', NULL, NULL, NULL, 'http://www.perkinelmer.com/pages/020/cellularimaging/products/volocityrestoration.xhtml ', NULL),
('2206', NULL, '0', NULL, 'Xuggler Slider', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Xuggler Slider ', NULL),
('2208', NULL, '0', NULL, 'affine', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2209', NULL, '0', NULL, 'blackTopHatGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2210', NULL, '0', NULL, 'bwlabel', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2211', NULL, '0', NULL, 'channel', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2212', NULL, '0', NULL, 'closing', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2213', NULL, '0', NULL, 'closingGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2214', NULL, '0', NULL, 'combine', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2215', NULL, '0', NULL, 'computeFeatures', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2216', NULL, '0', NULL, 'dilate', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2217', NULL, '0', NULL, 'dilateGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2218', NULL, '0', NULL, 'display', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2219', NULL, '0', NULL, 'distmap', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2220', NULL, '0', NULL, 'erode', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2221', NULL, '0', NULL, 'erodeGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2222', NULL, '0', NULL, 'fillHull', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2223', NULL, '0', NULL, 'filter2', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2224', NULL, '0', NULL, 'flip', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2225', NULL, '0', NULL, 'floodFill', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2226', NULL, '0', NULL, 'flop', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2227', NULL, '0', NULL, 'gblur', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2228', NULL, '0', NULL, 'graph cut library', NULL, NULL, NULL, 'http://cbia.fi.muni.cz/projects/graph-cut-library.html', NULL),
('2230', NULL, '0', NULL, 'ilastik - Carving', NULL, NULL, NULL, 'http://klimt.iwr.uni-heidelberg.de/', NULL),
('2232', NULL, '0', NULL, 'ilastik - Manual Tracking', NULL, NULL, NULL, 'http://klimt.iwr.uni-heidelberg.de/', NULL),
('2233', NULL, '0', NULL, 'ilastik - Object Classification', NULL, NULL, NULL, 'http://klimt.iwr.uni-heidelberg.de/', NULL),
('2234', NULL, '0', NULL, 'ilastik - Pixel Classification', NULL, NULL, NULL, 'http://klimt.iwr.uni-heidelberg.de/', NULL),
('2235', NULL, '0', NULL, 'localCurvature', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2236', NULL, '0', NULL, 'makeBrush', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2237', NULL, '0', NULL, 'medianFilter', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2238', NULL, '0', NULL, 'ocontour', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2239', NULL, '0', NULL, 'opening', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2240', NULL, '0', NULL, 'openingGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2241', NULL, '0', NULL, 'optic', NULL, NULL, NULL, 'http://cbia.fi.muni.cz/optic/', NULL),
('2242', NULL, '0', NULL, 'paintObjects', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2245', NULL, '0', NULL, 'resize', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2246', NULL, '0', NULL, 'restoreTools', NULL, NULL, NULL, 'http://mathcs.emory.edu/~nagy/RestoreTools/index.html', NULL),
('2247', NULL, '0', NULL, 'rgbImage', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2248', NULL, '0', NULL, 'rmObjects', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2249', NULL, '0', NULL, 'rotate', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2250', NULL, '0', NULL, 'selfcomplementaryTopHatGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2251', NULL, '0', NULL, 'stackObjects', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2252', NULL, '0', NULL, 'thresh', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2253', NULL, '0', NULL, 'tile', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2254', NULL, '0', NULL, 'translate', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2255', NULL, '0', NULL, 'transpose', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2256', NULL, '0', NULL, 'untile', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2258', NULL, '0', NULL, 'watershed', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2260', NULL, '0', NULL, 'writeImage', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2117', NULL, '0', NULL, 'Javascript', NULL, NULL, NULL, 'http://en.wikipedia.org/wiki/ECMAScript', NULL),
('2118', NULL, '0', NULL, 'JRuby', NULL, NULL, NULL, 'http://jruby.org/', NULL),
('2119', NULL, '0', NULL, 'Clojure', NULL, NULL, NULL, 'http://clojure.org/', NULL),
('2121', NULL, '0', NULL, 'C', NULL, NULL, NULL, 'http://en.wikipedia.org/wiki/C_(programming_language)', NULL),
('2116', '2116', '79', NULL, 'WIS-PhagoTracker', 'WIS-PhagoTracker is a software application for quantitative analysis of high throughput cell migration assay. The cell migration assay is based on a modified Phagokinetic tracks procedure, in which motile cells "leave their tracks" on a specialized surface. These tracks are visualized using a screening microscope.\r\n\r\nWIS-PhagoTracker enables morphometric analysis of such tracks. It uses multiscale segmentation algorithm for fine detection of tracks and cells boundaries. Following the segmentation step, it quantifies various morphometric parameters for each track, such as track area, perimeter, major and minor axis and solidity. All these measures are calculated for each track in each well of a well plate and saved for further statistical analysis\r\n\r\nWIS-PhagoTracker supports all the analysis phases starting from preprocessing, finding tracks of selected wells or a whole plate, through viewing the results and manually rejecting tracks to statistical analysis of the results. It also supports batch processing of several plates, and analysis of single image files. A user interface enables the user to modify the relevant parameters of the process, according to specific image\'s requirements. Results are exported into Excel readable files. \r\n\r\n', 'Ofra Golani, Meirav Galun, Suha Naffar Abu-Amara , Benjamin Geiger', NULL, 'http://www.weizmann.ac.il/vet/IC/software/wis-phagotracker', NULL),
('2102', NULL, '0', NULL, 'MATLAB', 'MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation programming language. Developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran.', NULL, NULL, 'http://www.mathworks.es/products/matlab/', NULL),
('1608', NULL, '0', NULL, 'Open Microscopy Environment (OME)', NULL, NULL, NULL, 'http://www.openmicroscopy.org/site', NULL),
('2262', NULL, '92', NULL, 'Forum', '<html>                                                                          \r\n    <body>  \r\n    <!-- was 900 x 700 -->                                                                \r\n    <iframe id="forum_embed"                                                    \r\n     src="javascript:void(0)"                                                   \r\n     scrolling="no"                                                             \r\n     frameborder="0"                                                            \r\n     width="700"                                                                \r\n     height="700">                                                              \r\n    </iframe>                                                                   \r\n                                                                                \r\n    <script type="text/javascript">                                             \r\n     document.getElementById("forum_embed").src =                               \r\n      "https://groups.google.com/forum/embed/?place=forum/bioimage-analysis-list" +\r\n      "&showsearch=true&showpopout=true&parenturl=" +                           \r\n      encodeURIComponent(window.location.href);                                 \r\n    </script>                                                                   \r\n    </body>                                                                     \r\n</html>', NULL, NULL, NULL, NULL),
('2267', '2267', '2', NULL, 'ImgLib2', 'ImgLib2 is a generic, multi-dimensional data processing library allowing for processing algorithms to be defined in a data-type, dimension, and container independent manner. Due to its interface-based design, it is easy to write adapters to virtually all existing data containers. It is the basis of KNIME, ImageJ2 and a couple of Fiji plugins.\r\n', 'Stephan Saalfeld, Stephan Preibisch, Tobias Pietzsch, Curtis Rueden, Barry DeZonia, Christian Dietz, Martin Horn, Mark Hiner, et al', NULL, 'http://imglib2.net/', NULL),
('2270', '2270', '2', NULL, 'Definiens Developer', 'Definiens is a commercial  image segmentation and classification tool. The user designs a signal processing workflow by combining built-in filtering, thresholding and object classification modules. Object detection is typically done on hierarchical object levels, e.g cell level for  cell objects and organelle level Nucleus and ER obejcts inside a cell object.\r\nFor each object, a huge set of features (shape-based, intensity based, relations to neighbor objects...) is available and can be used for object classification or merging with neighboring objects. \r\nThe classical definiens workflow is the so called bottom-up approach: In a first step, the image is segmented in numerous small objects, resulting in a heavy oversegmentation of of the target objects. Objects are then fused step by step on basis of features like “relative border to neighbor object” or “elliptic fit of resulting (fused) object”. Objects can assigned to different classes (like “nucleus” or “cancer cell”), based on their features. \r\n\r\nWeaknesses:\r\n-complex to use\r\n-closed (no API)\r\n-very expensive\r\n-relatively slow (you have to buy one license for each core)\r\n-bad 3D-visualization\r\n-time lapse analysis is possible but complicated\r\n\r\nStrengths:\r\n-powerful method to classify objects based on multiple features\r\n-2D data, especially histological data\r\n-good training material to learn software usage\r\n-detailed documentation\r\n', NULL, NULL, 'http://developer.definiens.com/', NULL),
('2276', '2276', '2', NULL, 'Matlab Image Processing Toolbox', 'The Matlab image processing toolbox extends the Matlab core functionality with general purpose image processing capabilities. This ranges from image access (read / write), common filters (convolution, morphology, order based, Wiener, feature extraction, image enhancement, ...), image transformation (rotation, affine transformation, ...) to segmentation algorithms (thresholding, watershed, region growing). There is also an extensive list of functions to deal with binary or label mask and perform for instance connected particle analysis or morphological operations.\r\n\r\nStrengths:\r\n- Most functions extend to nD\r\n- optimized functions (muti-threaded for some)\r\n- Matlab community (Matlab central)\r\n- relatively low entry-threshold for functionality\r\n- Tutorials & Webinars\r\n\r\nLimitations:\r\n- no embedded visualization of nD Microscopy data\r\n', NULL, NULL, 'http://www.mathworks.com/products/image/index.html', NULL),
('2278', '2278', '2', NULL, 'Schnitzcells', 'Schnitzcells is a MATLAB based software that allows for quantitative analysis of fluorescent time-lapse movies of living cells.  The software package is developed most specifically for bacteria and has been instrumental in analyzing  E.coli and B. subtilis movies.\r\nThe software contains functions that segment cells (based on either fluorescence or phase images),tracks cells in a frame-to-frame manner,build lineage trees and quantitatively extracts fluorescence.\r\nStrength: tools for manually editing segmentation and lineage, well documented, free matlab source code, sample data\r\nLimitations: changes need to be done directly in the matlab code, no support\r\n', NULL, NULL, 'http://easerver.caltech.edu/wordpress/schnitzcells/', NULL),
('2283', NULL, '137', NULL, 'Open Image Processing Blog (Matlab) ', 'This is an open blog, where people from the Image Processing community can present and share parts of their work, expose more people to it, inspire new ideas for projects and make new connections.\r\nThe goal is to create a database of useful and unique image processing posts. Most posts should be hands-on, with the MATLAB code of the problem and with clear, easy to follow documentation.\r\n\r\nA starting point for beginners and advanced when they encounter a new challenge or as a place to get ideas from.', NULL, NULL, 'http://www.imageprocessingblog.com', NULL),
('2259', NULL, '0', NULL, 'whiteTopHatGreyScale', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2284', NULL, '93', NULL, 'Analyzing fluorescence microscopy images with Fiji', '(quoted from the book) \r\nThe overall purpose of this book is to provide a path for busy biologists into understanding fluorescence images and how to analyze them, using the free, open-source software Fiji to explore the concepts. No background in image analysis or computer programming is assumed, nor is the maths here very deep. Specifically, if you are at ease with arithmetic, means, medians, standard deviations and the occasional square root, this is enough. There are a large number of questions and practicals strewn throughout the pages. In most cases, my suggested solutions are provided at the end of each chapter. You may well find alternative or better solutions.', NULL, NULL, 'http://go.qub.ac.uk/imagej-intro', NULL),
('2286', NULL, '92', NULL, 'The story so far', 'changes & additions I made:-\r\n* changed the title line of the site from "tag, tag, tag!" to "BioImage Informatics Index"\r\n* requested "context" module, and used it to add a custom front page describing the purpose of the site\r\n* possibly added a picture of some sushi knives (I thought it didn\'t work, which I was happy with, as I had second thoughts about it - perhaps I forgot to delete it? in my opinion it should be replaced by a nice bioimage analysis graphic)\r\n* added a Forum / Mailing list page that embeds the "bioimage analysis list" google group\r\n* re-arranged the Navigation menu\r\n* re-re-edited the re-edited landing page text to explain why there is a picture of some knives there', NULL, NULL, NULL, NULL),
('2285', NULL, '11', NULL, 'Added Update Notes', '<p>Since there are many administrators, I added this note section where you could tell others what addition / changes you made.</p>\r\n\r\n<p>For example, here I made this <strong>Note</strong>!</p>\r\n\r\n<p>Other changes I did already:</p>\r\n\r\n<ul>\r\n<li>Added a module "Active Tag", that allows automatic drop down list of tags.</li>\r\n<li>Added "Road Map", "About" and "Eubias", Basic page.</li>\r\n<li>Added a module "search_log"</li>\r\n<li>Added a module "taxonomy_formatter" for having better tag appearance but not actually working since if term is already added to the Tag field, I cannot change…</li>\r\n<li>Added a new field "self inroduction by users" to the content type peope. I did not know that spaces are allowed, so currently the item title looks ugly with underscores. I\'m sorry for this!</li>\r\n\r\n</ul>\r\n<p>Kota</p>', NULL, NULL, NULL, NULL),
('2287', NULL, '75', NULL, 'Languages Landing Page ', 'changed the Languages page type to "table".\r\ni think this is much nicer than the "unformatted list".\r\nsuggest to also consider this for the software packages page (maybe this could resolve the table discussion..).\r\ntischi\r\n ', NULL, NULL, NULL, NULL),
('1586', NULL, '0', NULL, 'Image Stabilizer', NULL, NULL, NULL, 'http://fiji.sc/Image_Stabilizer', NULL),
('2120', NULL, '0', NULL, 'Beanshell', NULL, NULL, NULL, 'http://beanshell.org/', NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2290', NULL, '11', NULL, 'Custom search', 'Added "Custom Search" to restrict search results to certain content type.\r\n\r\nBlock design is still ugly, CSS should be modified to make it look better (selection items are better be in every line, not inline. ) ', NULL, NULL, NULL, NULL),
('2291', '2291', '181', NULL, 'u-track', 'u-track is a multiple-particle tracking Matlab software that is designed to (1) track dense particle fields, (2) close gaps in particle trajectories resulting from detection failure, and (3) capture particle merging and splitting events resulting from occlusion or genuine aggregation and dissociation events. Its core is based on formulating correspondence problems as linear assignment problems and searching for a globally optimal solution.', 'LCCB', NULL, 'https://www.openmicroscopy.org/site/products/partner/u-track', NULL),
('2264', '2264', '2', NULL, 'OMERO', 'OMERO is a free, open source image management software. It is client-server based system which supports 5D images, including  big images and high-content screening data.  Data are stored on a server using relational database. They are accessed using 3 main clients, a desktop client, a web client and a command line tool. There are bindings from OMERO to other image analysis packages, like FLIMfit, OMERO.searcher.  The data in OMERO are organized in groups. A user can be a member of one or more groups. This groups can be collaborative or private, there are 4 levels of permissions to access/edit/annotate/delete the data of other users.\r\n\r\nThe package is supported not only by community forums, but also by a dedicated team which helps users to solve their problems and deals with the bugs submitted via error submission system.\r\n\r\n**Strengths:** Open source, scalable software, Supports diverse sets of imaging applications and domains (EM,LM, HCS, DigPath)\r\nCross-platform, Java-based application, API support for Java, Python, C++, Django, On-line Forums, Automatic QA and upload of software errors\r\nMulti-dimensional images, Web access, Free Demo-server accounts\r\n\r\n**Limitations:** Enterprise-scale software, so complex install, requires expertise, Actively developing API, Python scripts and functions still developing\r\n', 'OME Consortium; http://www.openmicroscopy.org/site/about/who-ome', NULL, 'http://www.openmicroscopy.org/', NULL),
('2196', NULL, '0', NULL, 'Particle Tracker 2D/3D', NULL, NULL, NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2292', NULL, '11', NULL, 'CellProfiler Pipeline', 'A CellProfiler pipeline file (*.cp) is a sequential set of image analysis modules automatically generated by CellProfiler as a text file. ', NULL, NULL, 'http://www.cellprofiler.org/CPmanual/Help_Using%20CellProfiler_How%20To%20Build%20A%20Pipeline.html', NULL),
('2293', NULL, '11', NULL, 'Better relationship, software / functions / workflows', '<p>A bit more work done for a better information flow.</p>\r\n\r\n<ol>\r\n<li><p>Added a schematic figure of information flow in Biii in the "<a href="http://biii.info/node/2282">Roadmap</a>" page.</p></li>\r\n<li><p>Modified Function and Workflow fields.</p>\r\n\r\n<ul>\r\n<li><p>"Required Software" needs complicated input, so I made a new field "Package/Library". The author now can select package/s and/or libraries  ( and in workflow  as well). "Required Software" will be hidden or deleted.</p></li>\r\n<li><p>See for example <a href="http://biii.info/node/2289">Cell segmentation and measurements</a> workflow page or <a href="http://biii.info/node/1926">3D Objects Counter</a> page.</p></li>\r\n<li><p>"Edit" tab will show you multiple choice now available for packages and languages. The list appearing here are those from Software Package nodes and Language nodes (as I could not find "Language" for CellProfiler Pipeline, I created a <a href="http://biii.info/node/2292">node(page)</a> for it).</p></li>\r\n<li>With these choices, it becomes more clear that each function belongs to somewhere: stand alone, single function program does not need "landing page".</li>\r\n<li><strong>Problem</strong>: [Workflow table] cannot be sorted. Tischi, how did you do that?</li>\r\n</ul>\r\n</li>\r\n<li><p>Added a new module Insert (\r\n<a href="https://drupal.org/project/insert">https://drupal.org/project/insert</a>) this will help you uploading images and insert them in body text.</p></li>\r\n<li><p>Reordered navigation, "Software Pacakges" pages is now hidden showing only its table.</p></li>\r\n<li><p>Removed Languages, Packages, Notes and Training from "Search" filtering at the top left.</p></li>\r\n</ol>\r\n', NULL, NULL, NULL, NULL),
('2297', '2297', '11', NULL, 'Bio7', '<p>Quote</p>\r\n\r\n<cite>\r\nThe application Bio7 is an integrated development environment for ecological modelling and contains powerful tools for model creation, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.\r\n</cite>', 'Dr. Marcel Austenfeld', NULL, 'http://bio7.org/', NULL),
('2265', '2265', '2', NULL, 'CellProfiler', 'CellProfiler is designed to enable biologists without training in computer vision or programming to quantitatively measure cell or whole-organism phenotypes from thousands of images automatically. The researcher creates an analysis pipeline from modules that find cells and cell compartments, measure features of those cells to form a rich, quantitative dataset that characterizes the imaged site in all of its heterogeneity.\r\nCellProfiler is structured so that the most general and successful methods and strategies are the ones that are automatically suggested, but the user can override these defaults and pull from many of the basic algorithms and techniques of image analysis to solve harder problems. CellProfiler is extensible through plugins written in Python or for ImageJ.\r\n\r\nStrengths: Cells, Neurons, C. Elegans, 2D Fluorescent images, high-throughput screening, phenotype classification, multiple stains/site, interoperability, extensibility, machine learning, segmentation\r\nLimitations: largely limited to 2D, not well suited to manually-guided analysis or content review, image size limitations\r\n', 'Lee Kamentsky', NULL, 'http://www.cellprofiler.org', NULL),
('2268', '2268', '2', NULL, 'PINK', 'Pink is an generic image processing library with inclination towards morphology, discrete topology, skeletonization and image segmentation. Operators support 2D, 3D and 4D images where there exist algorithms for such extensions. There is no practical limitation about the image site. It is suitable for general image processing or as a preprocessing / postprocessing toolset for specific image processing problems. \r\nThere are several successfull applications of PINK in the fields of Biology, Material Sciences, Education and Image Processing Research.\r\n\r\nAdvantages:\r\nPink has lot of specific operators of skeletonization, skeleton filtering, morphological filtering, segmentation, noise reduction as well as a huge body of general preprocessing and postprocessing operators which are usefull for everyday tasks. It is connected with basic 2D and 3D visualization tools. \r\nPink has operators in the following areas: Interactive IP operators, Arithmetic IP operators, Format and type conversion, Mathematical morphology, Digital connectivity, Digital topology (binary),\r\nDigital topology (grayscale), Orders topology, Geometrical operators, Graphic primitives, Histogram-based operators, Signal processing Statistics, Three-dimensional meshing, Visualization, Segmentation\r\n', 'Laszlo Marak, Hugues Talbot, Michel Couprie', NULL, 'http://pinkhq.com', NULL),
('2049', NULL, '0', NULL, 'A Unifying Parametric Framework for 2D Steerable Wavelet Transforms', NULL, NULL, NULL, 'http://bigwww.epfl.ch/demo/circular-wavelets/', NULL),
('2266', '2266', '2', NULL, 'Fiji', 'Fiji is just ImageJ: a distribution of ImageJ (and ImageJ2) together with Java, Java 3D and a lot of plugins organized into a coherent menu structure. The main focus of Fiji is to assist research in life sciences. It is a free, open-source, community-driven project.\r\n', NULL, NULL, 'http://fiji.sc/', NULL),
('2298', '2298', '11', NULL, 'Endrov', '<cite>\r\nEndrov started development in 2007 by Johan Henriksson in the group of Thomas Bürglin group / Karolinska insitutet. At that time it was merely a tool to support the analysis of C. elegans embryogenesis. It was decided to not base it on ImageJ because little of it could be reused, many of the problems came from the core design. Since then the scope of Endrov has expanded to be useful for all image processing and be able to replace ImageJ.\r\n</cite>', 'Johan Henriksson', NULL, 'http://www.endrov.net/', NULL),
('2273', '2273', '2', NULL, 'EBImage', 'EBImage is an open source image processing and analysis package for the statistical programming language R. Its primary goal is to enable automated analysis of large sets of images such as those obtained in high-throughput screening. An extensive range of basic image processing and feature extraction methods are provided. EBImage is available through the Bioconductor software project (www.bioconductor.org).\r\n\r\n<h3>Strengths</h3>\r\n<ul>\r\n<li>Lightweight</li>\r\n<li>Suitable for automated, scripted analyses</li>\r\n<li>All functions are documented with examples</li>\r\n<li>Modular links to R and Bioconductor software, notably imageHTS and cellHTS2</li>\r\n<li>Community support via the Bioconductor mailing list</li>\r\n<li>Reproducible (image) analysis using the Sweave report-writing system</li>\r\n</ul>', NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/html/EBImage.html', NULL),
('2299', NULL, '11', NULL, 'Added Markdown to text format', '### Text format addition\r\n\r\nAdded Markdown syntax for text editing. ([Module Markdown filter](https://drupal.org/project/markdown)). \r\n\r\n### Moving Packages / Libraries in "Functions" to "Packages / Libraries"\r\n\r\nAs Ricard and Seb pointed out, there are many softeware packages and libraries saved as "Functions". I moved some of them manually. \r\n\r\nThere are still a lot more...\r\n\r\n', NULL, NULL, NULL, NULL),
('1893', NULL, '0', NULL, 'Workbooks', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Workbooks', NULL),
('1572', NULL, '0', NULL, '3D Active Meshes', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/3D_Active_Meshes', NULL),
('2091', NULL, '0', NULL, '3D Area', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/3D_Area', NULL),
('1758', NULL, '0', NULL, '3D Generalized Riesz-Wavelet Toolbox for Matlab', NULL, NULL, NULL, 'http://bigwww.epfl.ch/demo/steerable-wavelets-3d/', NULL),
('1438', NULL, '0', NULL, 'Active Cells 3D', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Active_Cells_3D', NULL),
('2302', '2302', '137', NULL, 'Columbus Image Data Storage and Analysis System', 'Columbus is a combination of an image database (based on Omero, OME) and an image analysis engine based on Acapella (PerkinElmer). It is dedicated to cell culture based high content screening data and is used via a web interface.\r\nIt provides a set importers for automated microscopes such as Yokogawa CellVoyager, PerkinElmer Operetta, PerkinElmer Opera and data in Metamorph format. \r\n\r\nAfter login, Images can be explored in a standard web browser by clicking on a well plate view. Image analysis workflows can be developed by combining modules like "find nuclei", "find cytoplasm", "find spots" for object detection. Objects can have a hierarchical structure, e.g. spot objects can be part of a cell object. The approach of workflow design  is similar to the freeware cell profiler, but more restricted (less functions and less parameters to tweak) and easier to use. \r\n\r\nMutliple intensity- and shape based features can be calculated from detected objects (e.g. texture: haralick, Garbor, SER). Objects can be classified by these features by using hard thresholds or by supervised machine learning.\r\n\r\n\r\nAnalysis workflows and results are stored in the database and can be exprted as csv tables for secondary analysis. Simple secondary analysis workflows can be also applied in Columbus directly. Results can be visualized as heatmaps on the plate view. \r\nThe HCS statistics software Genedata Screener Assay Analyzer can be directly connected to the database. \r\n\r\n\r\n\r\n', 'PerkinElmer', NULL, 'http://www.perkinelmer.de/pages/020/cellularimaging/products/columbus.xhtml', NULL),
('1521', NULL, '0', NULL, 'Nucleus Counter', 'Bundled with MBF pacakge. ', NULL, NULL, 'http://fiji.sc/Nucleus_Counter', NULL),
('2087', NULL, '0', NULL, 'TurboReg', NULL, NULL, NULL, 'http://bigwww.epfl.ch/thevenaz/turboreg/', NULL),
('2306', '2306', '11', NULL, 'MATLAB', 'MATLAB is famous, so this page is only for being the landing page for components and workflows. ', NULL, NULL, 'http://www.mathworks.es/products/image/', NULL),
('2307', '2307', '11', NULL, 'IMOD', 'IMOD is a set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections. The package contains tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3-D data from any orientation, and modeling and display of the image files. ', 'David Mastronarde, Rick Gaudette, Sue Held, Jim Kremer, and Quanren Xiong', NULL, 'http://bio3d.colorado.edu/imod/', NULL),
('2308', '2308', '11', NULL, 'pythonxy', '**Python(x,y)** is a free scientific and engineering development software for numerical computations, data analysis and data visualization based on Python programming language, Qt graphical user interfaces and Spyder interactive scientific development environment.\r\n\r\nMany python libraries related to numerical calculation are packaged, so you do not need to search and install them individually. \r\n\r\nIncluded libraries are listed **[here](https://code.google.com/p/pythonxy/wiki/StandardPlugins).**\r\n', 'pierre.raybaut, grizzly.nyo', NULL, 'https://code.google.com/p/pythonxy/', NULL),
('1782', NULL, '0', NULL, 'pillow', 'A fork of PIL python package, with small collection of image import/export and image processing modules. \r\n\r\nSee [Reference Documentation](http://pillow.readthedocs.org/en/latest/reference/index.html) for more details. \r\n\r\nThough this package mostly works in any platform, some of them are limited to Windows. This package is a part of [pythonxy](https://code.google.com/p/pythonxy/). \r\n', NULL, NULL, 'https://github.com/python-imaging/Pillow', NULL),
('2304', NULL, '11', NULL, 'node_export, Advanced Form Block.', '- Added [Node Export](https://drupal.org/project/node_export). \r\n  - I thought this would be a good tool for moving packages in "Function (now called Compnent)" pages to "Software Package" page. \r\n  - Problem is that since field machine names are different in these content types, importing does not work well. \r\n\r\n- Added [Advanced Form Block](https://drupal.org/project/afb). \r\n  - This is an attempt to add tag (term) imput field within View page. \r\n  - test page: <http://biii.info/node/2288>\r\n  - problems\r\n    - page view without login does not allow access to node edit, so there are errors. \r\n    - This form block can only be specific to a node. Cannot add this as a block to content type. \r\n \r\n- The Front page text disappeared after adding Advanced Form Block module. For this reason, I created a new front page. \r\n ', NULL, NULL, NULL, NULL),
('2309', NULL, '11', NULL, 'Captcha', 'Added captcha module \r\n\r\n<https://drupal.org/project/captcha>\r\n\r\nto avoid spam user registrations (currently 30 or so per day...).\r\n\r\nWhile I was installing this, Biii blanked out (site could not be reloaded, always white.)\r\n\r\nSolution: from command line\r\n\r\n    drush dis feeds\r\n    drush cc #clears cache\r\n    drush en feeds.\r\n\r\n... A bit more detail:\r\n\r\nClearing cache with only feeds_ui being discabled returned error. \r\n\r\n>PHP Fatal error:  Class name must be a valid object or a string in /home/drupal/drupal->7.23/sites/biii.imagej.net/modules/contrib/feeds/includes/FeedsConfigurable.inc on line 59\r\n>Drush command terminated abnormally due to an unrecoverable error.   [error]\r\n>Error: Class name must be a valid object or a string in\r\n>/home/drupal/drupal-7.23/sites/biii.imagej.net/modules/contrib/feeds/includes/FeedsConfigurable.inc,\r\n>line 59\r\n\r\n"feeds" should be disabled, not pnly feeds_ui. \r\n\r\n---\r\n\r\nI further added "Recaptcha" module, to have more modern captcha. \r\n\r\nhttps://drupal.org/project/reCAPTCHA\r\n\r\nLooks even more nice now. \r\n\r\n\r\n\r\n', NULL, NULL, NULL, NULL),
('2313', NULL, '11', NULL, 'easy tag cloud, recaptcha, advanced form block', '## Tag Clouds\r\n\r\nIt\'s been a bit sad having seen no tag cloud so I added an express version of the tag cloud. You probably have already recognized it! This uses a module called "tagadelic". \r\n\r\n<https://drupal.org/project/tagadelic>\r\n\r\nIt has many modules on top of it such as [cumulus](https://drupal.org/project/cumulus), and I installed it but it did not work... maybe there are too many tags. \r\n\r\n## Patched Advanced Form Block\r\n\r\nI already [installed this module](http://biii.info/node/2304) to edit tags in View mode several days ago, but it has a problem that the tag editor can only be added to a specific node, and not possible to do that for a general content type. Today I tried a patch provided by a guy in the Drupal site:\r\n\r\n<https://drupal.org/node/2005086>\r\n\r\nApplying this patch, it seems to work well now with "Workflow" nodes. We could test this for several days. The tag editor is at the moment placed at the bottom of workflow pages. See for expample:\r\n\r\n [Microtubules Tools (3D)](http://biii.info/node/2312)\r\n\r\n## Added a new field to Components.\r\n\r\nTo actualize the Roadmap figure, I added a "Workflow" link to the content type "Components" (this used to be "functions"). An example case is here:\r\n\r\n[AnalyzeSkeleton](http://biii.info/node/1976)\r\n\r\nThe ImageJ plugin written by Ignacio is used by Volker\'s "Microtubules Tool (3D)", so this is now linked in the "AnalyzeSkelton" page. Evebtually, this link should be automatically detected by workflow page (or maybe vice versa?) by workflow page and reciprocal links should then be established. \r\n\r\n\r\n## Recaptcha\r\n\r\nI installed a [module named captcha](https://drupal.org/project/captcha) yesterday but it only blocked 10% or so of spam registrations (about 100 a day and it\'s increasing). I then upgraded it by adding a module recaptcha:\r\n\r\n<https://drupal.org/project/recaptcha> \r\n\r\nNow, after sevral hours, this module already blocked 69 spamming attempts. I show you here the log during last one hour:\r\n\r\n    CAPTCHA	10/30/2013 - 21:21	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 21:13	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 21:13	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 21:13	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 21:13	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 21:00	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:58	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:43	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:42	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:39	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:28	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:17	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:17	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:17	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)	\r\n    CAPTCHA	10/30/2013 - 20:17	user_register_form post blocked by CAPTCHA module:...	Anonymous (not verified)\r\n\r\n Very effective!', NULL, NULL, NULL, NULL),
('1591', NULL, '0', NULL, '3D OrthoViewer', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/3D_OrthoViewer', NULL),
('1579', NULL, '0', NULL, 'Manual Tracking (ImageJ)', '<p>An ImageJ plugin for manually tracking objects by mouse clicking. </p>\r\n\r\nSee <a href="http://rsbweb.nih.gov/ij/plugins/track/track.html">plugin page in ImageJ website</a>', 'Fabrice Cordelieres', NULL, 'http://fiji.sc/Manual_Tracking', NULL),
('2244', NULL, '0', NULL, 'readImage (EBImage)', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2315', NULL, '11', NULL, '"What links here"', 'To provide a better view on relationships between Components and Workflows, I added a block called "what links here" to content types component and workflow. You could find them at the bottom of left side bar. \r\n\r\nSee following two workflow pages:\r\n\r\n<http://biii.info/node/2310>\r\n\r\n<http://biii.info/node/2312>\r\n\r\nComponents used in these workflows are automatically **"backlinked"**, since those component pages specify these workflows.\r\n\r\nAnother type of automatic linking will be "similar to" automatic links. This is planned to be done by evaluating overlap in tag terms. \r\n\r\n', NULL, NULL, NULL, NULL),
('2123', '2123', '16', NULL, 'CellCognition', '## Introduction\r\n\r\nCellCognition is a computational framework dedicated to the automatic analysis of live cell imaging data in the context of High-Content Screening (HCS). It contains algorithms for segmentation of cells and cellular compartments based on various fluorescent markers, features to describe cellular morphology by both texture and shape, tools for visualizing and annotating the phenotypes, classification, tracking and error correction. Events such as mitosis can be automatically identified and aligned to study the temporal kinetics of various cellular processes during cell cycle. CellCognition can be used by novices in the field of image analysis and is applicable to hundreds of thousands of images by parallelization on compute clusters with minimal effort. The tool has been successfully applied to quantitative phenotypic profiling of cell division, yet machine learning enables CellCognition to be used for the analysis of other dynamic processes. \r\n\r\n## Backends\r\n\r\nFollowing libraries are used:\r\n\r\n* numpy\r\n* [VIGRA](http://biii.info/node/2109)\r\n* PyQT\r\n* hdf5\r\n* matplotlib\r\n* sklearn\r\n  * Machine Learning in Python', 'Held Michael, Thomas Walter, Sommer Christoph, Rudolf Hoefler', NULL, 'http://www.cellcognition.org', NULL),
('2316', NULL, '11', NULL, 'Find Focused Slices', '\r\nAn ImageJ plugin for selecting a plane in focus among multiple slices image stack. The algorithm uses normalized variance. \r\n\r\nA short tutorial is available in the plugin web page (above). ', 'Qingzong TSENG', NULL, 'https://sites.google.com/site/qingzongtseng/find-focus', NULL),
('2317', '2317', '11', NULL, 'Volocity', '-', NULL, NULL, 'http://www.perkinelmer.com/pages/020/cellularimaging/products/volocity.xhtml', NULL),
('2109', '2109', '103', NULL, 'VIGRA', 'VIGRA is a free C++ and Python library that provides fundamental image processing and analysis algorithms. Its generic architecture allows it to be used in many different application contexts and ecosystems. It is designed as an intelligent library (using the C++ template mechanism) which allows users to write code at a fairly high level of abstraction and optimizes away the abstraction overhead upon compilation. It can therefore work efficiently on very large data and forms the basis of ilastik and CellCognition.\r\n\r\n<b>Strengths:</b> open source, high quality algorithms, unlimited array dimension, arbitrary pixel types and number of channels, high speed, well tested, very flexible, easy-to-use Python bindings, support for many common file formats (including HDF5)\r\n\r\n<b>Limitations:</b> no GUI, C++ not suitable for everyone, BioFormats not supported, parallelization requires external control\r\n\r\n<b>Images and Multi-dimensional Arrays:</b>\r\n<ul>\r\n    <li>\r\n        templated image data structures for arbitrary pixel types,\r\n        fixed-size vectors\r\n    </li>\r\n    <li>\r\n        multi-dimensional arrays for arbitrary high dimensions\r\n    </li>\r\n    <li>\r\n        pre-instantiated images with many different scalar and vector valued pixel types\r\n        (byte, short, int, float, double, complex, RGB, RGBA etc.)\r\n    </li>\r\n    <li>\r\n        2-dimensional image iterators, multi-dimensional iterators for arbitrary high dimensions, adapters for various image and array subsets\r\n    </li>\r\n    <li>\r\n        input/output of many image file formats: Windows BMP, GIF, JPEG, PNG, PNM, Sun Raster, \r\n        TIFF (including 32bit integer, float, and double pixel types and multi-page TIFF), \r\n        Khoros VIFF, HDR (high dynamic range), Andor SIF, OpenEXR\r\n    </li>\r\n    <li>\r\n        input/output of images with transparency (alpha channel) into suitable file formats.\r\n    </li>\r\n    <li>\r\n        comprehensive support for HDF5 (input/output of arrays in arbitrary dimensions)\r\n    </li>\r\n    <li>\r\n        continuous reconstruction of discrete images using splines: Just create\r\n        a SplineImageView of the desired order and access interpolated values and \r\n        derivative at any real-valued coordinate.\r\n    </li>\r\n</ul>\r\n<b>Image Processing:</b>\r\n<ul>\r\n    <li>\r\n        STL-style image processing algorithms with functors (e.g. arithmetic and algebraic\r\n        operations, gamma correction, contrast adaptation, thresholding), arbitrary regions \r\n        of interest using mask images\r\n    </li>\r\n    <li>\r\n        image resizing using resampling, linear interpolation, spline interpolation etc.\r\n    </li>\r\n    <li>\r\n        geometric transformations: rotation, mirroring, arbitrary affine transformations\r\n    </li>\r\n    <li>\r\n        automated functor creation using expression templates\r\n    </li>\r\n    <li>\r\n        color space conversions: RGB, sRGB, R\'G\'B\', XYZ, L*a*b*, L*u*v*, Y\'PbPr, Y\'CbCr, Y\'IQ, and Y\'UV\r\n    </li>\r\n    <li>\r\n        real and complex Fourier transforms in arbitrary dimensions, \r\n        cosine and sine transform (via fftw)\r\n    </li>\r\n    <li>\r\n        noise normalization according to F&ouml;rstner\r\n    </li>\r\n    <li>\r\n        computation of the camera magnitude transfer function (MTF) via the\r\n        slanted edge technique (ISO standard 12233)\r\n    </li>\r\n</ul>\r\n<b>Filters:</b>\r\n<ul>\r\n    <li>\r\n        2-dimensional and separable convolution, Gaussian filters and their derivatives, \r\n        Laplacian of Gaussian, sharpening etc.\r\n    </li>\r\n    <li>\r\n        separable convolution and FFT-based convolution for arbitrary dimensional data\r\n    </li>\r\n    <li>\r\n        resampling convolution (input and output image have different size)\r\n    </li>\r\n    <li>\r\n        recursive filters (1st and 2nd order), exponential filters\r\n    </li>\r\n    <li>\r\n        non-linear diffusion (adaptive filters), hourglass filter\r\n    </li>\r\n    <li>\r\n        total-variation filtering and denoising (standard, higer-order, and adaptive methods)\r\n    </li>\r\n    <li>\r\n        tensor image processing: structure tensor, boundary tensor, gradient energy tensor,\r\n        linear and non-linear tensor smoothing, eigenvalue calculation etc. (2D and 3D)\r\n    </li>\r\n    <li>\r\n        distance transform (Manhattan, Euclidean, Checker Board norms, 2D and 3D)\r\n    </li>\r\n    <li>\r\n        morphological filters and median  (2D and 3D)\r\n    </li>\r\n    <li>\r\n        Loy/Zelinsky symmetry transform \r\n    </li>\r\n    <li>\r\n        Gabor filters\r\n    </li>\r\n</ul>\r\n<b>Segmentation:</b>\r\n<ul>\r\n    <li>\r\n        edge detectors: Canny, zero crossings, Shen-Castan, boundary tensor\r\n    </li>\r\n    <li>\r\n        corner detectors: corner response function, Beaudet, Rohr and F&ouml;rstner corner detectors\r\n        tensor based corner and junction operators\r\n    </li>\r\n    <li>\r\n        region growing: seeded region growing, watershed algorithm\r\n    </li>\r\n</ul>\r\n<b>Image Analysis:</b>\r\n<ul>\r\n    <li>\r\n        connected components labeling (2D and 3D)\r\n    </li>\r\n    <li>\r\n        detection of local minima/maxima (including plateaus, 2D and 3D)\r\n    </li>\r\n    <li>\r\n        tensor-basesd image analysis (2D and 3D)\r\n    </li>\r\n    <li>\r\n        powerful incremental computation of region and object statistics\r\n    </li>\r\n</ul>\r\n<b>3-dimensional Image Processing and Analysis:</b>\r\n<ul>\r\n    <li>\r\n        point-wise transformations, projections and expansions in arbitrary high dimensions \r\n    </li>\r\n    <li>\r\n        all functors (e.g. regions statistics) readily apply to higher dimensional data as well \r\n    </li>\r\n    <li>\r\n        separable convolution and FFT-based convolution filters, resizing, morphology, and Euclidean distance transform for arbitrary dimensional arrays (not just 3D)\r\n    </li>\r\n    <li>\r\n        connected components labeling, seeded region growing, watershed algorithm for volume data\r\n    </li>\r\n</ul>\r\n<b>Machine Learning:</b>\r\n<ul>\r\n    <li>\r\n        random forest classifier with various tree building strategies\r\n    </li>\r\n    <li>\r\n        variable importance, feature selection (based on random forest)\r\n    </li>\r\n    <li>\r\n        unsupervised decomposition: PCA (principle component analysis) and \r\n        pLSA (probabilistic latent semantic analysis)\r\n    </li>\r\n</ul>\r\n<b>Mathematical Tools:</b>\r\n<ul>\r\n    <li>\r\n        special functions (error function, splines of arbitrary order, integer square root, chi square distribution, elliptic integrals)\r\n    </li>\r\n    <li>\r\n        random number generation\r\n    </li>\r\n    <li>\r\n        rational and fixed point numbers\r\n    </li>\r\n    <li>\r\n        quaternions\r\n    </li>\r\n    <li>\r\n        polynomials and polynomial root finding\r\n    </li>\r\n    <li>\r\n        matrix classes, linear algebra, solution of linear systems, eigen system computation, singular value decomposition\r\n    </li>\r\n    <li>\r\n        optimization: linear least squares, ridge regression, L1-constrained least squares (LASSO, non-negative LASSO, least angle regression), quadratic programming\r\n    </li>\r\n</ul>\r\n<b>Inter-language support:</b>\r\n<ul>\r\n    <li>\r\n        Python bindings in both directions (use Python arrays in C++, call VIGRA functions from Python)\r\n    </li>\r\n    <li>\r\n        Matlab bindings of some functions\r\n    </li>\r\n</ul>\r\n', 'Ullrich Koethe and many contributors', NULL, 'http://hci.iwr.uni-heidelberg.de/vigra/', NULL),
('2318', '2318', '11', NULL, 'Schnitzcells', 'Schnitzcells is a MATLAB based software that allows for quantitative analysis of fluorescent time-lapse movies of living cells. The software package is developed most specifically for bacteria and has been instrumental in analyzing  E.coli and B. subtilis movies.\r\nThe software contains functions that segment cells (based on either fluorescence or phase images),tracks cells in a frame-to-frame manner,build lineage trees and quantitatively extracts fluorescence.\r\n\r\n## Dependencies\r\n\r\n[MATLAB Image Processing Toolbox](http://biii.info/node/2276)', NULL, NULL, 'http://easerver.caltech.edu/wordpress/schnitzcells/', NULL),
('2319', '2319', '11', NULL, 'OpenMOLE', '\r\nOpenMOLE (Open MOdeL Experiment) is a workflow engine designed to leverage the computing power of distributed execution environments for naturally parallel processes. A process is told naturally parallel if the same computation runs many times for a set of different inputs, such as model experiment or data processing… It is free software distributed under the AGPLv3 free software license.', NULL, NULL, 'http://www.openmole.org/', NULL),
('2320', '2320', '11', NULL, 'OpenCV', 'OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Adopted all around the world, OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 6 million. Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics.', NULL, NULL, 'http://opencv.org/', NULL),
('2321', '2321', '11', NULL, 'Amira', '-', NULL, NULL, 'http://www.vsg3d.com/amira/overview', NULL),
('2322', '2322', '11', NULL, 'AutoQuant', '-', NULL, NULL, 'http://www.mediacy.com/index.aspx?page=AutoQuant', NULL),
('2323', '2323', '11', NULL, 'Avizo', '\r\nWherever three-dimensional data sets need to be processed, in materials science, geosciences, environmental or engineering applications, Avizo offers abundant state-of-the-art features within an intuitive workflow and easy-to-use graphical user interface.\r\nAvizo is packaged in different Editions, with optional eXtension modules. Each Avizo Edition delivers tailored user interface and specific feature-set for each application area:\r\n\r\n[Avizo Standard](http://www.vsg3d.com/avizo/standard)\r\n\r\n[Avizo Earth](http://www.vsg3d.com/avizo/earth)\r\n\r\n[Avizo Wind](http://www.vsg3d.com/avizo/wind)\r\n\r\n[Avizo Green](http://www.vsg3d.com/avizo/green)\r\n\r\n[Avizo Fire](http://www.vsg3d.com/avizo/fire)', NULL, NULL, 'http://www.vsg3d.com/avizo/overview', NULL),
('2324', '2324', '11', NULL, 'CATMAID', '**Collaborative Annotation Toolkit for Massive Amounts of Image Data**\r\n\r\nCATMAID is a Collaborative Annotation Toolkit for Massive Amounts of Image Data. It is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by GoogleMaps, with which it shares basic navigation concepts, enhanced to allow the exploration of 3D biological image data acquired by optical or physical sectioning microscopy techniques. The interface enables seamless sharing of regions of interest through bookmarks and synchronized navigation through multiple registered data sets.\r\n\r\nWith massive biological image data sets it is unrealistic to create a sustainable centralized repository. A unique feature of CATMAID is its partially decentralized architecture where the presented image data can reside on any Internet accessible server and yet can be easily cross-referenced in the central database. In this way no image data are duplicated and the data producers retain full control over their images.\r\n\r\nCATMAID is intended to serve as data sharing platform for biologists using high-resolution imaging techniques to probe large specimens. Any high-throughput, high-content imaging project such as gene expression pattern screens would benefit from the interface for data sharing and annotation.', 'Stephan Saalfeld, Albert Cardona, Volker Hartenstein, Pavel Toman?ák', NULL, 'http://fly.mpi-cbg.de/~saalfeld/catmaid/', NULL),
('2271', '2271', '2', NULL, 'FARSIGHT', 'A General purpose image processing toolkit written in C++ based on ITK, VTK, Qt, and Boost. Main features: algorithms for cell segmentation, cell tracing, cell tracking, and vessel tracing. Registration and mosaicing algorithms for large scale datasets. Visualization tools actively linked to inspect and edit results.\r\nStrengths:\r\n- Open-source, free, multi platform, code is highly parallelized, uses git for version control\r\n- Large scale processing, also efficient visualization of such datasets.\r\n- Active learning module for classification\r\n- Most of the algorithms have been extended to handle 16-bit images, and 3D Images.\r\n- Possibility to create complex pipelines thanks to it’s modular architecture\r\n- Editing tools are designed to save the editing operation which can later be used to validate the algorithms performance\r\n- Advance preprocessing algorithms like curvelets, tensor voting, and wrappers around ITK-algorithms\r\n- Multiple viewers included to inspect results such as: Histograms, scatter plots, tables, kymograph, all of them linked together.\r\n- Strong emphasis to work on multichannel images (up to 40 channels)\r\n- Rich number of cell features included \r\nWeakness:\r\n- GUI is suboptimal compared to commercial packages.\r\n- Tracking module requires an external library CPLEX.\r\n- No support for brightfield images\r\n- No native interoperability with other software packages\r\n- More documentation needed / tutorial needed\r\n', NULL, NULL, 'http://www.farsight-toolkit.org/', NULL),
('2325', '2325', '11', NULL, 'GNU Octave', '"GNU Octave is a high-level language, primarily intended for numerical computations."*\r\nIt can also be used for image processing, statistics and plotting as well as Matlab.\r\n"GNU Octave is also freely redistributable software. You may redistribute it and/or mod-\r\nify it under the terms of the GNU General Public License as published by the Free Software\r\nFoundation."*\r\n\r\n* <http://math.hawaii.edu/lab/197/octave.pdf>', NULL, NULL, 'http://www.gnu.org/software/octave/', NULL),
('2269', '2269', '2', NULL, 'Icy', '<p>Reproducing an experiment doesn’t stop at the bench when images are concerned.  <a href="http://icy.bioimageanalysis.org/">Icy is an open source bioimaging software package</a> that aims to provide a framework for authors to share, and others to reproduce, research once the sample hits the microscope.  Icy was released in April 2011 and is being developed at the Quantitative Image Analysis Unit at the Pasteur Institute in France by Jean-Christophe Olivo-Marin and his team.  The goal is to provide standardized software architecture, with a visual programming framework and online repository of plugins and protocols, brought together with sophisticated content-management and communication systems for such extended reproducible research.</p>\r\n<p>Icy provides intuitive user interfaces for graphical protocol development for image acquisition, analysis and storage that are easy to use for biologists and developers alike.  Developers should find that Icy’s ‘EzPlug’ API library, versioning, and auditing tools make creating a custom plugin from most any source easy.  Users will find the automatic error reporting, central repository and on-line community hub great for storing and sharing plugins and protocols.  Icy is even developing a cloud-computing framework to address the scalability issues of high-content screening.</p>\r\n<p>As of this writing there are <a href="http://icy.bioimageanalysis.org/list">207 plug-ins 50 scripts and 14 protocols</a> available for download, including those for microscope control, particle tracking, three dimensional segmentation, and even spot detection using wavelets.</p>\r\n<p>Published in Nature Methods (<a href="http://www.nature.com/nmeth/journal/v9/n7/full/nmeth.2075.html">Nat Methods 9(7):690-6 (2012)</a>).\r\nIcy can be downloaded at <a href="http://icy.bioimageanalysis.org/">http://icy.bioimageanalysis.org/</a></p>\r\n<h3>Strength:</h3>\r\n<ul>\r\n<li>Open-source.</li>\r\n<li>Centralized repository of 205 plugins, 50 scripts and 14 protocols</li>\r\n<li>Rate and comment plugins</li>\r\n<li>5D</li>\r\n<li>Search and install features directly from Icy</li>\r\n<li>Graphical programming with protocols</li>\r\n<li>Write scripts in javascript or python</li>\r\n<li>Automatic bug reports</li>\r\n<li>Native ImageJ integration 100% compatible</li>\r\n<li>Native Micro-Manager integration</li>\r\n<li>Share your plugins and protocols online</li>\r\n<li>Can run headless</li>\r\n<li>Intuitive user interface</li>\r\n<li>Online management of plugins</li>\r\n<li>Connect Icy to Matlab</li>\r\n<li>Interactive widgets</li>\r\n<li>Build your graphical interface with EzPlug</li>\r\n<li>Use the power of your graphic card with OpenCL</li>\r\n<li>Loaded with 20 up-to date libs</li>\r\n</ul>\r\n<h3>Weaknesses</h3>\r\n<ul>\r\n<li>No tutorial for plugins writing..yet</li>\r\nSee here:<ul>\r\n<li>http://icy.bioimageanalysis.org/index.php?display=devDoc</li>\r\n<li>http://icy.bioimageanalysis.org/index.php?display=detailTag&tagId=29</li>\r\n<li>and here:\r\nhttp://icy.bioimageanalysis.org/index.php?display=startDevWithIcy</li>\r\n<li>and also here:\r\nhttp://icy.bioimageanalysis.org/index.php?display=startDevWithIcy</li>\r\n</ul></li>\r\n<li>Image size limited to 2GigaByte per single  2D channel (means that an image of 40.000x40.000 can be handle by Icy. Still big !) Still you can have a stack of 100000x40Kx40kxUnlimited number of channel if you have RAM. Will be improved</li>\r\n</ul>', 'Fabrice de Chaumont and Stephane Dallongeville', NULL, 'http://icy.bioimageanalysis.org', NULL),
('1436', '1436', '80', NULL, 'KNIME Image Processing and Analysis', 'KNIME is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting. Its an open integration platform and provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network. One of these extensions adds the ability for image analysis allowing to process, segment and further analyze images which can easily be used in combination with the other extensions, potentially from other fields.', NULL, NULL, 'http://tech.knime.org/community/image-processing', NULL),
('2272', '2272', '2', NULL, 'Mahotas', 'This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.\r\n\r\nMahotas is appropriate for users comfortable with programming or builders of end-user tools.\r\n\r\n==== Strengths\r\n\r\nThe major strengths are in speed and quality of documentation. Almost all of the functionality is implemented in for multiple dimensions. It can be used with other Python packages which provide additional functionality.\r\n\r\nMahotas and all packages on which it relies are open-source.\r\n\r\n==== Considerations\r\n\r\nA complete environment will require installing multiple packages.', 'Luis Pedro Coelho', NULL, 'http://mahotas.rtfd.org/', NULL),
('2277', '2277', '2', NULL, 'Matlab Computer Vision System Toolbox', 'The Matlab Computer Vision System Toolbox extends the Matlab core functionality with general purpose image processing functions for feature detection & extraction, object detection & tracking and motion estimation.\r\n\r\nStrengths:\r\n- Most functions extend to nD\r\n- optimized functions (muti-threaded for some)\r\n- Matlab community (Matlab central)\r\n- relatively low entry-threshold for functionality\r\n- Tutorials & Webinars\r\n\r\nLimitations:\r\n- no embedded visualization of nD Microscopy data\r\n', NULL, NULL, 'http://www.mathworks.com/products/computer-vision/index.html', NULL),
('2327', '2327', '11', NULL, 'Metamorph', '-', NULL, NULL, 'http://www.moleculardevices.com/products/software/meta-imaging-series/metamorph.html', NULL),
('2328', '2328', '11', NULL, 'Micro-Manager', '-', NULL, NULL, 'http://valelab.ucsf.edu/~MM/MMwiki/', NULL),
('2279', '2279', '2', NULL, 'ITK', '<p>ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis. Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data. It is widely used and contributed in the medical imaging field.</p>\r\n\r\n<h3>Strengths</h3>\r\n<ul>\r\n<li>Highly optimized C++, well commented</li>\r\n<li>Consistently updated (new) algorithms</li>\r\n<li>many tools and softwares are built upon it</li>\r\n<li>connected with VTK</li>\r\n<li>Insight Journal (open code and sample data)</li>\r\n<li>Extensive list of examples & tutorials</li>\r\n</ul>\r\n<h3>Limitations</h3>\r\n<ul>\r\n<li>yet detached from the bioimage analysis world</li>\r\n<li>hard to use for end users without development skills</li>\r\n</ul>', NULL, NULL, 'http://itk.org/', NULL),
('2329', '2329', '11', NULL, 'ITK-SNAP', '-', NULL, NULL, 'http://www.itksnap.org', NULL),
('2098', '2098', '79', NULL, 'WIS-NeuroMath', 'WIS-NeuroMath - is a software tool for automated analysis and quantification of fluorescent microscopy images of Nerve cells, in both in vivo and in vitro preparations. It allows for accurate detection of neurites in challenging images. Following neurite detection, different types of processing can be carried: Cell Morphology of cultured neurons, Neurite Length Analysis and Ganglion Explant Analysis. \r\nUsefull also for angiogenesis analysis.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n', 'Ofra Golani, Meirav Galun, Ida Rishal, Michael Fainzilber', NULL, 'http://www.weizmann.ac.il/vet/IC/software/wis-neuromath', NULL),
('2330', '2330', '11', NULL, 'ImagePro', '\r\nImage-Pro software includes the latest tools for scientific and industrial image analysis and image processing. Capture, process, measure, share, visualize and compare.\r\n\r\nImagePro Insight\r\n\r\n<http://www.mediacy.com/index.aspx?page=IP_Insight>\r\n\r\nImagePro Priemier\r\n\r\n<http://www.mediacy.com/index.aspx?page=Products>', NULL, NULL, 'http://www.mediacy.com/index.aspx?page=Image_Pro_Software', NULL),
('2331', '2331', '11', NULL, 'SimuCell', 'An open-source framework to synthetically generate fluorescent microscopic images of cellular population.', NULL, NULL, 'http://www4.utsouthwestern.edu/altschulerwulab/simucell/', NULL),
('2332', '2332', '11', NULL, 'softWoRx 5.0', 'softWoRx® 5.0 is the latest in DeltaVision image acquisition software. Find samples, acquire images and process data quickly and efficiently with the intuitive user interface. softWoRx 5.0 includes visualization and measurement tools and exports data in a wide variety of formats making it a versatile tool for more than just image acquisition.', NULL, NULL, 'http://www.api.com/softworx.asp', NULL),
('2333', '2333', '11', NULL, 'v3dlib', 'v3dlib library is a cross-platform C++ library written in wxWidgets for displaying 3D images. The most important part of the library is a window that can display 3D images of various types (RGB, RGB16, GRAY8, GRAY16, and float). The window can easily be extended of a new functionality and is used in many applications (e.g., viewer3d, Acquiarium, batchcrop, and others).', NULL, NULL, 'http://cbia.fi.muni.cz/projects/viewing-3d-images-with-v3dlib.html', NULL),
('2334', '2334', '11', NULL, 'Vaa3D', 'Vaa3D [1, 2, 3] is a handy, fast, and versatile 3D/4D/5D Image Visualization & Analysis System for Bioimages & Surface Objects. It also provides many unique functions ... It is also Open Source, supports a very simple and powerful plugin interface and thus can be extended & enhanced easily... \r\n\r\n        Vaa3D is a cross-platform (Mac, Linux, and Windows) tool for visualizing large-scale (gigabytes, and 64-bit data) 3D image stacks and various surface data. It is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management. This makes Vaa3D suitable for various bioimage informatics applications, and a nice platform to develop new 3D image analysis algorithms for high-throughput processing. In short, Vaa3D streamlines the workflow of visualization-assisted analysis.\r\n\r\n        In our latest development, Vaa3D can render 5D (spatial-temporal) data directly in 3D volume-rendering mode; it supports convenient and interactive local and global 3D views at different scales... it even has a Matlab file IO toolbox. Probably most importantly, you can now write your own plugins to take advantage of the Vaa3D platform!', 'Hanchuan Peng', NULL, 'http://www.vaa3d.org', NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2335', '2335', '11', NULL, 'XuvTools', 'XuvTools (pronounced “ex-you-vee-tools”) is a fully automated 3D stitching software for biomedical image data, typically confocal microscopy images. XuvTools runs on Microsoft Windows XP and Vista, Linux and Apple Mac computers. It supports 32 and 64bit operating systems (with 64bit highly preferred). XuvTools is free and open source software (see Licensing), so you can start using it immediately. Go to Downloads and give it a try.\r\n\r\nThe goal of XuvTools is to provide tools, that combine multiple microscopic recordings to obtain a larger field of view (“stitching”) and a higher dynamic range (“HDR” recombination), or better resolution (multi view reconstruction), and to make these tools publicly available.', NULL, NULL, 'http://www.xuvtools.org', NULL),
('2337', NULL, '11', NULL, 'Orthogonal Views', 'A menu item in ImageJ that allows you to inspect a 3D stack with orthogonal views (XY, XZ, YZ planes). Slicing plane could be interactively moved by dragging crosses. \r\n\r\nPedro Almada wrote a plugin to save the current orthogonal views as montage. \r\n\r\n## ImageJ Macro Usages\r\n\r\n[Save orthogonal views | OrthoSaver](http://uic.igc.gulbenkian.pt/macros/OrthoSaver.ijm)\r\n\r\n>ImageJ\'s orthogonal viewer for 3D stacks doesn\'t let you easily save the current orthogonal view. This macro, once installed, lets users create a montage with the currently open orthogonal views and selection guides. To use it, open an image z-stack and open the orthogonal viewer. With the mouse, choose which are the sections of interest to you and without moving the mouse, press F2. You\'ll get a montage of the currently selected orthogonal view.\r\n\r\n\r\n\r\n', NULL, NULL, 'http://rsbweb.nih.gov/ij/docs/guide/146-28.html#toc-Subsubsection-28.6.10', NULL),
('1746', NULL, '0', NULL, 'Image Convolution', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2336', NULL, '11', NULL, 'many moved from "Components" to "software"', 'Many pages typed "Compnent" were moved to "Software / Libraries". \r\n\r\nSee\r\n\r\n<https://docs.google.com/spreadsheet/ccc?key=0AvV4TLDrGd3SdHU0NERBYlBWTHY4WWNSTmtocnpQV3c&usp=drive_web#gid=0>\r\n\r\nFor the list of pages which were moved. \r\n\r\nFor some software packages / libraries, pages existed in both so tags were merged. I wrote a short script for doing this: see\r\n\r\n<https://gist.github.com/cmci/7243213 >', NULL, NULL, NULL, NULL),
('2343', NULL, '11', NULL, 'ijblob', '## Features\r\n\r\n>The IJBlob library indentifying connected components in binary images. The algorithm used for connected component labeling is:\r\n\r\n>Chang, F. (2004). A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002\r\n\r\n##Reference\r\n\r\nWagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. Journal of Open Research Software 1(1):e6, DOI: <http://dx.doi.org/10.5334/jors.ae>', 'Thorsten Wagner', NULL, 'https://code.google.com/p/ijblob/', NULL),
('2229', NULL, '0', NULL, 'ilastik - Automatic Tracking', NULL, NULL, NULL, 'http://klimt.iwr.uni-heidelberg.de/', NULL),
('2344', '2344', '3', NULL, 'python-javabridge', 'The javabridge Python package makes it easy to start a Java virtual\r\nmachine (JVM) from Python and interact with it. Python code can\r\ninteract with the JVM using a low-level API or a more convenient\r\nhigh-level API.\r\n\r\nPyPI record: https://pypi.python.org/pypi/javabridge\r\n\r\nDocumentation: http://pythonhosted.org/javabridge/\r\n\r\nGitHub repository: https://github.com/CellProfiler/python-javabridge\r\n\r\nReport bugs here: https://github.com/CellProfiler/python-javabridge/issues\r\n\r\npython-javabridge is licensed under the BSD license. See the\r\naccompanying file LICENSE for details.\r\n', 'Vebjorn Ljosa, Lee Kamentsky, Johannes Schindelen', NULL, 'https://github.com/CellProfiler/python-javabridge', NULL),
('2345', '2345', '3', NULL, 'python-bioformats', 'Python-bioformats is a Python wrapper for Bio-Formats, a standalone\r\nJava library for reading and writing life sciences image file formats.\r\nBio-Formats is capable of parsing both pixels and metadata for a large\r\nnumber of formats, as well as writing to several formats.\r\nPython-bioformats uses the python-javabridge to start a Java virtual\r\nmachine from Python and interact with it. Python-bioformats was\r\ndeveloped for and is used by the cell image analysis software\r\nCellProfiler (cellprofiler.org).\r\n\r\nPyPI record: https://pypi.python.org/pypi/python-bioformats\r\n\r\nDocumentation: http://pythonhosted.org/python-bioformats/\r\n\r\nGitHub repository: https://github.com/CellProfiler/python-bioformats\r\n\r\nReport bugs here: https://github.com/CellProfiler/python-bioformats/issues\r\n\r\npython-bioformats is licensed under the GPL license to be compatible with the copy of Bio-Formats that is distributed with the package, but is compatible with a BSD license if loci_tools.jar is replaced with SCIFIO jars. See the\r\naccompanying file LICENSE for details.\r\n', 'Lee Kamentsky, Vebjorn Ljosa', NULL, 'https://github.com/CellProfiler/python-bioformats', NULL),
('2352', NULL, '137', NULL, 'Tracking of Microtububule Tips ', 'The workflow contains a Matlab package (plusTipTracker) for segmentation and tracking of microtubules, based on fluorescence time-lapse movies from microtrubule tip markers such as EB-GFP.\r\nThe tracking model accounts for the specific movement characteristics of microtubules\r\n\r\nMoreover, scripts for secondary analysis of detected microtubule paths are provided.', 'Kathryn T. Applegate, Sebastien Besson, Alexandre Matova, Maria H. Bagonis, Khuloud Jaqaman, Gaudenz Danuser', NULL, NULL, NULL),
('2354', NULL, '468', NULL, 'ActionBar ', 'An ActionBar is a simple annotated text document that has snippets of Imagej macros or Beanshell arranged into buttons.\r\n\r\nThis tool is very useful when creating custom work flows integrating multiple components. Each component can be linked to a button for a more streamlined and accessible workflow.', 'Jérôme Mutterer ', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:utilities:action_bar:start', NULL),
('2366', NULL, '139', NULL, 'Quantitative analysis of focal adhesion dynamics', 'The quantification is explained in detail, however the code is not published.', 'Christoph Möhl, Norbert Kirchgessner, Claudia Schäfer, Bernd Hoffmann and Rudolf Merkel', NULL, NULL, NULL),
('2368', '2368', '472', NULL, 'Aphelion', 'A commercial image analysis software.\r\nIt\'s interface allows to easily perform measurements and image analysis. Your actions can be recorded and a macro (in a basic script language) can then be created. Almost no knowledge in programming is needed. You can also use python.\r\nA SDK is also available to develop stand alone applications in c++.\r\nAdditional modules allow to use specific operations (3D operators...\r\n\r\nExamples of available categories of operators : filtering, edge detection, mathematical morphology, segmentation, Frequency operations, mathematical/logical operations, measurements...', 'ADCIS', NULL, 'http://www.adcis.net/en/Image-Processing-And-Analysis-Software-And-Custom-Engineering-Developments.html', NULL),
('2370', NULL, '94', NULL, 'Protein Micro-array Analysis', NULL, 'Sébastien Tosi', NULL, NULL, NULL),
('2375', NULL, '94', NULL, 'Blood vessel segmentation and network analysis', 'This macro segments blood vessels in a 3D stack. It is suited for well contrasted images (low background) and works better if the width of the vessels of interest is reasonably uniform.', 'Sébastien Tosi', NULL, NULL, NULL),
('2377', NULL, '94', NULL, 'Tissue analysis from histological sections', 'This macro batch processes all the 2D images (tif and jpg files) located in a user defined folder by calling Fiji Weka trainable segmentation to classify each pixel, and reports the areas of each class in a human readable results table. The classifier to be applied to each image should be previously trained on a representative image by an expert and exported to file (Save classifier) into the image folder to be processed.', 'Sébastien Tosi', NULL, NULL, NULL),
('2380', NULL, '94', NULL, 'SPIM de-striper', 'This macro implements a filter that is meant to attenuate close to parallel intensity stripes in an image, such as often happening in light sheet microscopy. The results are usually decent even when the stripes show a large angular spread due to light sheet refraction at the sample surface. The filter can process a 3D stack but the processing is performed slice by slice.', 'Sébastien Tosi', NULL, NULL, NULL),
('2381', NULL, '94', NULL, 'Tube un-winder', 'This macro can be used to un-wide a tubular structure and flatten its surface (like peeling of and flattening the skin of a banana). The macro can only process a single channel 3D stack but it is easy to process multiple channels by exporting and importing ROI manager selections. Technically the macro computes the radial average intensity projection inside a ring centred on the radial symmetry axis of the object. The final image is a radial mapping of the intensity (radial angle along X, axial length along Y).', 'Sébastien Tosi', NULL, NULL, NULL),
('2384', NULL, '94', NULL, '(Z,T) Hyperstack Stitcher', 'This macro builds a stitched image from a muti-position 3D + time hyperstack. The XY positions of the montage should be coded as channels in the input  hyperstack. Channel ordering can be configured in the dialog box to adapt to Column/Row and Meander/Comb configurations: The images should appear in this order when browsing the hyperstack with the channel slider. Fine stitching is supported (requires sufficient overlap between the views). The XY displacements of each field of view for stitching are computed for a single reference (Z,T) slice (user configurable) and applied to all slices (Z and T).', 'Sébastien Tosi', NULL, NULL, NULL),
('2385', NULL, '94', NULL, '(Z,T,C) Stitcher', 'This macro can stitch a (Z,T,C) data set with virtually no limit on the number of Z slices and time frames. The input to the macro is a folder with the raw tiff images (one image per file) as typically exported by motorized microscopes. These files must all be stores in the same folder and the file naming should ideally comply to OME-TIFF. The macro is however quite flexible: Only --X, --Y and --Z fields with user defined number of digits are compulsory. --T, --C and --L fields with user defined number of digits are necessary for multiple time frames / channels data sets. A compatible data set is provided as a .zip archive. Before processing it unzip it to a given location.\r\n\r\nThe stitching is performed in a reference Z slice (and in a specific reference time frame and channel). The same displacements are applied to all the Z slices, time frames and channels. Before starting the batch processing a montage with the original images of the selected Z slice / time frame / channel is displayed together with the stitched image in this stack. If you are not satisfied with the result you can select another reference. The stitching is then performed time frame by time frame and slice by slice and the stitched images are exported to a single user defined output folder.  \r\n\r\nThe macro can also process a data set with multiple channels, the stitching is then computed once on a reference channel and then applied to the other channels.', 'Sébastien Tosi', NULL, NULL, NULL),
('2231', NULL, '0', NULL, 'ilastik - Density Counting', 'This workflow estimates (densely distributed) object counts by the density of objects in the image without performing segmentation or object detection. Current version only works for 2D images of roundish objects with similar sizes on relatively homogeneous background. Users should provide a few labels of background and objects (especially on clustered objects), and the tool predicts the density of objects on the entire image. Counting is then estimated by integrating the density values on the whole image or specified rectangular regions of interests.\r\n ', 'ilastik team', NULL, 'http://ilastik.org/documentation/counting/counting.html', NULL),
('2387', NULL, '94', NULL, 'Interactive Volume of Interest Extractor in Large 3D Stacks', 'This macro allows to interact with a large, single channel, z-stack (possibly exceeding the main memory of the computer) and to extract a volume of interest by marking several reference points.', 'Sébastien Tosi', NULL, NULL, NULL),
('2388', NULL, '94', NULL, 'Image denoising', 'If your images are corrupted by a strong dominant Gaussian noise you can try this simple filter. It is based on thresholding in the DCT domain and is usually vastly superior to typical Gaussian filtering in term of detail preservation / noise reduction trade-off. The filter unfortunately introduces some block like artifacts that can be mitigated by averaging out overlaping shifted windows (as implemented in the Matlab version) and performing maximum intensity projection after the filtering: As such the filter is way more adapted to process 3D stacks that you plan to maximum intensity project than to process single z slice images.', 'Sébastien Tosi', NULL, NULL, NULL),
('2379', NULL, '444', NULL, 'Morphological Segmentation', 'Morphological Segmentation is an ImageJ/Fiji plugin that combines morphological operations, such as extended minima and morphological gradient, with watershed flooding algorithms to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D. \r\n\r\nMorphological Segmentation runs on any open grayscale image, single 2D image or (3D) stack. If no image is open when calling the plugin, an Open dialog will pop up.\r\n\r\nThe user can pan, zoom in and out, or scroll between slices (if the input image is a stack) in the main canvas as if it were any other ImageJ window. On the left side of the canvas there are three panels of parameters, one for the input image, one with the watershed parameters and one for the output options. All buttons, checkboxes and input panels contain a short explanation of their functionality that is displayed when the cursor lingers over them.\r\n\r\nImage pre-processing: some pre-processing is included in the plugin to facilitate the segmentation task. However, other pre-preprocessing may be required depending on the input image. It is up to the user to decide what filtering may be most appropriate upstream. ', 'Ignacio Arganda-Carreras, David Legland ', NULL, 'http://fiji.sc/Morphological_Segmentation', NULL),
('2383', NULL, '444', NULL, 'Marker-controlled Watershed', 'Marker-controlled Watershed is an ImageJ/Fiji plugin to segment grayscale images of any type (8, 16 and 32-bit) in 2D and 3D based on the marker-controlled watershed algorithm (Meyer and Beucher, 1990). This algorithm considers the input image as a topographic surface (where higher pixel values mean higher altitude) and simulates its flooding from specific seed points or markers. A common choice for the markers are the local minima of the gradient of the image, but the method works on any specific marker, either selected manually by the user or determined automatically by another algorithm. \r\n\r\nMarker-controlled Watershed needs at least two images to run:\r\n    The Input image: a 2D or 3D grayscale image to flood, usually the gradient of an image.\r\n    The Marker image: an image of the same dimensions as the input containing the seed points or markers as connected regions of voxels, each of them with a different label. They correspond usually to the local minima of the input image, but they can be set arbitrarily.\r\nAnd it can optionally admit a third image:\r\n    The Mask image: a binary image of the same dimensions as input and marker which can be used to restrict the areas of application of the algorithm. Set to "None" to run the method on the whole input image.\r\nRest of parameters:\r\n    Calculate dams: select to enable the calculation of watershed lines.\r\n    Use diagonal connectivity: select to allow the flooding in diagonal directions.\r\n', 'Ignacio Arganda-Carreras, David Legland ', NULL, 'http://fiji.sc/Marker-controlled_Watershed', NULL),
('2390', NULL, '139', NULL, '2D and 3D mitochondrial shape analysis and network properties', 'The original paper [publication 1] describes a method to analyze mitochondrial morphology in 2D and 3D and is based on previous work of the authors [publication 2].', 'Julie Nikolaisen, Linn I. H. Nilsson, Ina K. N. Pettersen, Peter H. G. M. Willems, James B. Lorens, Werner J. H. Koopman, Karl J. Tronstad ', NULL, NULL, NULL),
('2389', NULL, '75', NULL, 'Object Tracking', 'This example shows an example of object tracking. This pipeline analyzes a time-lapse experiment to identify the cells and track them from frame to frame, which is challenging since the cells are also moving. In addition, this pipeline also extracts metadata from the filename and uses groups the images by metadata in order to independently process several sequences of images and output the measurements of each. ', 'Cellprofiler team', NULL, NULL, NULL),
('2391', NULL, '94', NULL, 'Cells tracking in tissue', 'Plot the centroid tracks and area evolution of the cells of a tissue with membrane labelling.', 'Sébastien Tosi', NULL, NULL, NULL),
('2289', NULL, '75', NULL, 'Cell segmentation and measurements', 'Human HT29 cells are fairly smooth and elliptical. This CellProfiler workflow demonstrates how to accurately identify these cells and how to measurements cellular parameters such as morphology, count, intensity and texture.\r\n\r\n\r\n', NULL, NULL, NULL, NULL),
('2396', NULL, '75', NULL, 'Human cytoplasm-nucleus translocation assay ', 'In this human cytoplasm-nucleus translocation assay, learn how to load a previously calculated illumination correction function for two separate channels, measure protein content in the nucleus and cytoplasm, and calculate the ratio as a measure of translocation. This is a clumpy cell type, so studying the settings in primary object identification may be helpful for users interested in the more advanced options that module offers. More about these images can be found at the BBBC.', 'Cellprofiler team', NULL, NULL, NULL),
('2398', NULL, '469', NULL, 'Icy Protocol', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/protocol/list', NULL),
('2400', NULL, '468', NULL, 'VSI File Extractor', 'This tool allows for extraction of image series from Olympus Slide Scanners. These VSI files usually contain several images that are too big to load into memory (>50k x 50k pixels).\r\n\r\nIt was written and tested on Fiji and is available from a Fiji Update Site: \r\nhttp://fiji.sc/List_of_update_sites\r\n', 'O.Burri, R. Guiet', NULL, 'http://biop.epfl.ch/TOOL_VSI_Reader.html', NULL),
('2131', NULL, '0', NULL, 'Background Subtractor', 'Histogram-based background subtractor for ImageJ.\r\nThe implemented algorithm is based on the assumption that, compared to the background region, object (foreground) regions are small. The plugin builds local histograms and assumes the most occuring intensity to be part of the background.', 'Janick Cardinale', NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2402', NULL, '469', NULL, 'Icy Plugin', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/index.php?display=startDevWithIcy', NULL),
('2403', NULL, '93', NULL, 'Interactive Density Counting using ilastik', 'This workflow estimates (densely distributed) object counts by the density of objects in the image without performing segmentation or object detection. Current version only works for 2D images of roundish objects with similar sizes on relatively homogeneous background. Users should provide a few labels of background and objects (especially on clustered objects), and the tool predicts the density of objects on the entire image. Counting is then estimated by integrating the density values on the whole image or specified rectangular regions of interests.', 'ilastik team', NULL, NULL, NULL),
('2404', NULL, '92', NULL, 'Noise Suppression using Simple Spatial Filters', 'Simple spatial filters can be used to suppress noise in raw image data (i.e. by averaging intensities). The best choice of filter depends on the nature of the noise, but Gaussian filtering works well for Poisson noise (i.e. commonly observed photon-counting shot noise); whereas a median filter is ideal for salt-and-pepper noise. A larger filter radius leads to stronger noise suppression but more blurring. The URL above describes the simple 2D spatial filters available in ImageJ, but similar filters are available in most software. For 3D data, 3D versions of these filters work best (since there are more pixels to average within the same radius).', 'Graeme Ball', NULL, NULL, NULL),
('2405', NULL, '139', NULL, 'Measuring confluence in adherent cell cultures', 'This publication describes a very simple protocol to acquire images of adherent cell cultures over time and how to process these images in ImageJ to measure the area fraction (confluence).', 'Steven Busschotsa, Sharon O’Tooleb, John J. O’Learya, Britta Stordal', NULL, NULL, NULL),
('2399', NULL, '469', NULL, 'Icy Script', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/script/list', NULL),
('2406', NULL, '93', NULL, 'Automatic 2D/3D Tracking using ilastik', 'This workflow is used to track multiple (appear/disappear, dividing and merging) objects in presumably big 2D+t or 3D+t datasets. It is best suitable for roundish objects or spots. Tracking is done through segmentation, which can be obtained from ilastik pixel classification, or imported from other tools. Users should provide a few object level labels, and the software predicts results on the rest of the image or new images with similar image characteristics. As a result, all objects get assigned random IDs at the first frame of the image sequence and all descendants in the same track (also children objects such as daughter cells) inherit this ID.', 'ilastik team', NULL, NULL, NULL),
('2392', NULL, '467', NULL, 'White Balance ', 'The Macro processes a composite picture in ImageJ/Fiji.\r\nTo calculate the white balance, a rectangle at coordinates (x=100, y=100) and of size (w=100 pixels, h=100 pixels) is used. These values can be changed to make sure that a background region is taken for the calculation in the line: makeRectangle(100,100,100,100). The user could be prompted to draw the region by removing the signs // in the line: // waitForUser("Please draw a region in the background");', 'Laurent Gelman', NULL, NULL, NULL),
('1580', NULL, '0', NULL, 'BUnwarpJ', '2D Image registration method based on elastic deformations represented by B-splines.', 'Ignacio Arganda-Carreras', NULL, 'http://fiji.sc/BUnwarpJ', NULL),
('2415', NULL, '139', NULL, 'Single particle tracking', '>TrackMate provides the tools to perform single particle tracking (SPT). SPT is an image analysis challenge where the goal is to segment and follow over time some labelled, spot-like structures. Each spot is segmented in multiple frames and its trajectory is reconstructed by assigning it an identity over these frames, in the shape of a track. These tracks can then be either visualized or yield further analysis results such as velocity, total displacement, diffusion characteristics, division events, etc...', 'Nick Perry, Jean-Yves Tinevez, Johannes Schindelin', NULL, NULL, NULL),
('2358', NULL, '444', NULL, 'Blob segmentation', 'Simple macro to separates blobs\r\n\r\nLoad the Blobs sample image\r\nRun the plugin Morphological Segmentation\r\nDisplay the overlaid ', 'Ignacio Arganda-Carreras', NULL, NULL, NULL),
('2418', NULL, '426', NULL, 'ImageJ Macro Programming for Biological Image Analysis', 'Learn how to write ImageJ macros for the automation of image analysis tasks. For each topic there is a short presentation followed by a small exercise that has to be solved by the participants. Ideally you have already used ImageJ before but you know nothing or very little about programming. If this is not the case you might still find parts of the workshop interesting. Most of the workshop is available in the form of macro toolsets that you can install in FIJI. The macro toolsets provide predefined parts of the code that the student has to complete in order to solve an exercise.\r\n', NULL, NULL, 'http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/ImageJ_Macro_Programming_for_Biological_Image_Analysis', NULL),
('1560', NULL, '0', NULL, 'Register Virtual Stack Slices', 'This plugin takes a sequence of image slices stored in a folder, and delivers a list of registered image slices (with enlarged canvas). One of the images in the sequence can be selected by the user as reference and it will remain intact.\r\n\r\nThe plugin can perform 6 types of image registration techniques:\r\n\r\n* Translation\r\n* Rigid (translation + rotation)\r\n* Similarity (translation + rotation + isotropic scaling)\r\n* Affine\r\n* Elastic (via bUnwarpJ with cubic B-splines)\r\n* Moving least squares\r\n\r\nAll models are aided by automatically extracted SIFT features. \r\n\r\n[Source code](https://github.com/trakem2/register_virtual_stack_slices)', 'Albert Cardona, Ignacio Arganda-Carreras and Stephan Saalfeld', NULL, 'http://fiji.sc/Register_Virtual_Stack_Slices', NULL),
('2421', NULL, '471', NULL, '3D estimation of synaptic vesicle distribution in serial section TEM (ssTEM)', 'An estimate of the shortest distance of vesicles to synaptic cleft is computed in 3D for serial section TEM. Unfortunately the the authors do not provide an implementation.\r\n\r\nMethod:\r\n1. Bias correction for inhomogene lighting\r\n2. Image registration of TEM sections / stacks\r\n3. Detection of vesicles & synaptic cleft (semi-automatic)\r\n4. Compute distances in 3D\r\n', 'A Great Guy', NULL, NULL, NULL),
('2350', NULL, '471', NULL, 'FLIM / SLIM Analysis', 'An exponential curve fitting library used for Fluorescence Lifetime Imaging (FLIM) and Spectral Lifetime Imaging (SLIM), available as:\r\n\r\n* [Source code as C/C++ and Java library](https://github.com/slim-curve)\r\n* [Standalone Open-Source Windows application](https://www.assembla.com/spaces/ATD_TRI/wiki/Home)\r\n* [ImageJ plugin](http://fiji.sc/SLIM_Curve)\r\n\r\nPublications:\r\n\r\n* [Barber, P.R., Proc. SPIE, 5700, 2005.pdf](http://users.ox.ac.uk/~atdgroup/publications/Barber,%20P.R.,%20Proc.%20SPIE,%205700,2005.pdf)\r\n* [Barber, P.R., J. R. Soc. Interface, 6, 2009.pdf](http://users.ox.ac.uk/%7Eatdgroup/publications/Barber,%20P.R.,%20J.%20R.%20Soc.%20Interface,%206,%202009.pdf)\r\n', 'Paul Barber', NULL, NULL, NULL),
('2422', NULL, '470', NULL, 'Counting foci', 'ImageJ\'s built-in <em>Process -> Find Maxima</em> command can be used for many 2D counting-related tasks.\r\n\r\nFor counting small, bright foci (dots), set <em>Output type</em> to be <em>Point Selection</em>.  If too many points are detected, the number may be reduced using one or more of the following methods:\r\n<ul>\r\n<li>Apply a filter to reduce noise, e.g. <em>Process -> Filters -> Gaussian Blur...</em> prior to running <em>Find Maxima</em></li>\r\n<li>Set a minimum threshold with <em>Image -> Adjust -> Threshold...</em> prior to running <em>Find Maxima</em>, then use the <em>Above lower threshold</em> option within the dialog box</li>\r\n<li>Increase the <em>Noise tolerance</em> value (which effectively acts as a local threshold)</li>\r\n</ul>\r\n\r\nThe resulting point selection can be modified (points added/removed) by the <a href="http://rsbweb.nih.gov/ij/docs/guide/146-19.html#sec:Multi-point-Tool">Multi-Point tool</a>. \r\n\r\nAfter the points are available, final measurements can be made using <em>Analyze -> Measure</em>.', 'Michael Schmid, Wayne Rasband', NULL, NULL, NULL),
('2423', NULL, '93', NULL, 'Pixel Classification using ilastik', 'This workflow classifies, or segments, the pixels of an image given user annotations. It is especially suited if the objects of interests are visually (brightness, color, texture) distinct from their surrounding. Users can iteratively select pixel features and provide pixel annotations through a live visualization of selected feature values and current prediction responses. Upon users\' satisfaction, the workflow then predicts the remaining unprocessed image(s) regions or new images (as batch processing). Users can export (as images of various formats): selected features, annotations, predicted classification probability, simple segmentation, etc.\r\n\r\nThis workflow is often served as one of the first step options for other workflows offered by ilastik, such as <a href="http://biii.info/?q=node/2410">object classification</a>, <a href="http://biii.info/?q=node/2406">automatic tracking</a>.', 'ilastik team', NULL, NULL, NULL),
('2410', NULL, '93', NULL, 'Object Classification using ilastik', 'This workflow classifies objects based on object-level features (e.g. intensity based, morphology based, etc) and user annotations. It needs segmentation images besides the raw image data. Segmentation images can be obtained from <a href="http://biii.info/?q=node/2423">ilastik pixel classification</a>, or binary segmentation images from other tools. Within the object classification, one can prefilter objects through thresholds (on pixel probability image) or object sizes (on segmentation image). Outputs are predicted classification label images. Selected features can also be exported. Advanced users also have possibilities to add customized (object) features for classification in a simple plugin fashion through python scripts.', 'ilastik team', NULL, NULL, NULL),
('2047', NULL, '0', NULL, 'ClassifyPixels', NULL, NULL, NULL, 'http://www.cellprofiler.org/CPmanual/ClassifyPixels.html', NULL),
('2394', NULL, '137', NULL, 'Measure distance of organelles from nucleus in 3D', 'Below as simple worklow is descibed to measure subcellular localizations relative to the nucleus. \r\nYou may e.g. quantify how far some type of vesicles or protein aggregates are apart from the nucleus border. The forflow describes a solution for 3D data.\r\n\r\nData requirements:\r\n\r\n3D data, 2 channels\r\nChannel 1: nucleus stain\r\nChannel 2: stain for marker you want to quantify the distance to nucleus for\r\n\r\n\r\nWorkflow:\r\n\r\n1. Nucleus detection\r\n-Add a new SURFACE object, name it "nuclei"\r\n-Follow the object detection wizard to segment nucleus objects\r\n\r\n2. Marker object detection\r\n-Add a new SURFACE object\r\n-Follow the object detection wizard to segment nucleus objects\r\n\r\n3. Creating of distance map channel\r\n-In the image processing menu, go to SurfacesFunctions>>Distance Transformation\r\nNow Matlab should start\r\n-select nucleus objects and "distance outside objects"\r\nA new image channel should be crated now by the Matlab script\r\n\r\n4. Distance measurement\r\nThe generated distance map channel represents the distance from the nucleus border in pixel values. Thus, the distance of an organelle from the nucleus is equivalent to its mean gray value of the distcance map channel.  \r\nFor distance measurement, just export the mean gray value of the distance channel for each object.\r\n\r\nPlease note:\r\nIn the described workflow, the distance is always calculated to the closest nucleus border. This could be also the nucleus of a neighboring cell, which generates some error. A more complex approach to avoid this error would incorporate a cell segmentation step to assign certain organelle objects to certain cells. Therefore, a cell region marker is needed.\r\n\r\n\r\n\r\n', 'A Great Guy', NULL, NULL, NULL),
('2424', '2424', '139', NULL, 'OpenSlide', '>OpenSlide is a C library that provides a simple interface to read whole-slide images (also known as virtual slides). Python and Java bindings are also available. The Python binding includes a Deep Zoom generator and a simple web-based viewer. The Java binding includes a simple image viewer.', 'Adam Goode, Benjamin Gilbert, Jan Harkes, Drazen Jukic, M. Satyanarayanan', NULL, 'http://openslide.org/', NULL),
('1702', NULL, '0', NULL, 'ImarisMeasurementPro', NULL, NULL, NULL, 'http://www.bitplane.com/imaris/measurementpro', NULL),
('2426', NULL, '444', NULL, 'Batch spot detection with custom output', 'Download the protocol,use and modify in Icy.\r\nIt permits to detect spot with wavelet spot detector block.\r\n\r\nInput : loop on a folder \r\nOutputs: excel, binary, and detection screenshot', 'Fabrice de Chaumont', NULL, NULL, NULL),
('2040', NULL, '0', NULL, 'Wavelet Spot Detector Block', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Wavelet Spot Detector Block', NULL),
('2428', NULL, '11', NULL, 'Experimenters\' guide to colocalization studies: finding a way through indicators and quantifiers, in practice.', NULL, NULL, NULL, NULL, NULL),
('2429', NULL, '444', NULL, 'Optical Flow', 'This protocol computes the optical flow of a 2D+T sequence.\r\n\r\nThe results are displayed with flow arrows painted on top of the original sequence, and also with two additional sequences for the norm of the flow and a color-coded presentation of the flow following the Middlebury convention.', 'Timothée Lecomte', NULL, NULL, NULL),
('2430', NULL, '469', NULL, 'ZVI Extractor', '<blockquote>This macro allows to batch convert .zvi multichannel time-lapse movies into .tif stacks. There are several options for processing and filtering. In particular, you can register the jitter due to small stage movements during acquisition. For this option to work you need to install Kang Li\'s Image Stabilizer plugin.</blockquote>\r\n\r\n\r\n\r\n<em>Requires <a href="http://loci.wisc.edu/software/bio-formats">Bio-Formats</a> plugins and <a href="http://www.cs.cmu.edu/~kangli/code/Image_Stabilizer.html">Image Stabilizer</a></em>\r\n\r\n\r\n', 'A Great Guy', NULL, NULL, NULL),
('2362', NULL, '137', NULL, '3D cell tracking and quantification of shape changes', 'The workflow includes segmentation, tracking and quantifying morphological dynamics of moving cells in 3D. \r\n\r\nThe authors have implemented the workflow in Matlab, but so far there is no download link provided. To apply this workflow, we recommend to contact the authors or to implement the worflow based on the detailed description in the <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871028/">original paper</a>.\r\n\r\n', 'Du CJ, Hawkins PT, Stephens LR, Bretschneider T', NULL, NULL, NULL),
('2003', NULL, '0', NULL, 'Optical Flow - Horn-Schunck', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Optical Flow - Horn-Schunck', NULL),
('2081', NULL, '0', NULL, 'Flow Display', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Flow Display', NULL),
('2432', NULL, '139', NULL, 'Whole-slide histology analysis pipeline scripts', 'The publication describes a set of command-line tools to assist with the automated histology analysis of whole-slide images. This includes tools to organize the data, perform tiling and subsequent batch processing of the generated tiles in a cell profiler pipeline. All the tools are designed to run on a single PC or on a HPC system.\r\n\r\nThe scripts in the toolkit are on github under MIT licence.', 'Bastiaan G. L. Nelissen mail,    Joost A. van Herwaarden,   Frans L. Moll,   Paul J. van Diest,   Gerard Pasterkamp', NULL, NULL, NULL),
('2433', NULL, '444', NULL, 'Track cell intensity', 'Tracks a cell in a 2D video using active contours, and produces a list of ROI where intensity is measured and reported into a workbook.\r\nThe cell must be first delineated with a ROI in the first image of the video.', 'Alexandre Dufour', NULL, NULL, NULL),
('2434', NULL, '469', NULL, 'Image anonymisation', '<blockquote>This macro copies all images from one folder to another, randomizing names but keeping channels from the same image grouped. This is useful for blind quantification of images.</blockquote>', 'Christophe Leterrier', NULL, NULL, NULL),
('2425', NULL, '469', NULL, 'LIF Extractor', '<blockquote>This macro extracts .lei and .lif multichannel Z-stacks into multiple .tif stacks, splitting the channels into different stacks. Several options are possible such as background substraction, various filters, or optional reset of spatial scale.</blockquote>\r\n\r\n\r\n<em>Requires <a href="http://loci.wisc.edu/software/bio-formats">Bio-Formats</a></em> plugin', 'Christophe Leterrier', NULL, NULL, NULL),
('1659', NULL, '0', NULL, 'Active Contours', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/Active_Contours', NULL),
('1471', NULL, '0', NULL, 'Colocalization Analysis', 'Colocalization analysis requires sufficient back ground knowledge. see <bib>2428</bib>. ', NULL, NULL, 'http://fiji.sc/Colocalization_Analysis', NULL),
('2440', NULL, '469', NULL, 'Autofocusing in computer microscopy: selecting the optimal focus algorithm.', NULL, NULL, NULL, NULL, NULL),
('2372', NULL, '469', NULL, 'Autofocus', '<blockquote>Autofocus hyperstack macro\r\nSelect the in focus frame from each slice of a hyperstack and create a new stack\r\nof just the in focus frames.\r\n\r\nBased on algorithm F-11 "Normalized Variance"</blockquote>\r\n\r\nSee: <bib>2440</bib>', 'Richard Mort, original macro by Andy Weller http://imagejdocu.tudor.lu/doku.php?id=macro:normalized_variance', NULL, NULL, NULL),
('2442', NULL, '444', NULL, 'Separate image channel in folder', 'This protocol takes a folder containing images as input and extract each channel in a separate sub folder.', ' Stephane Dallongeville', NULL, NULL, NULL),
('2443', '2443', '139', NULL, 'scikit-image', 'Image processing library for Python\r\n\r\n>The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a versatile set of image processing routines. It is written in the Python language. This SciKit is developed by the SciPy community. All contributions are most welcome!', 'Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle Gouillart, Tony Yu and the scikit-image contributors. ', NULL, 'http://scikit-image.org/', NULL),
('2413', NULL, '426', NULL, 'Leaf Infection Tools', 'The Leaf Infection Tools allow to measure the area of leaves, of two stainings in different channels and of the overlap region of the two stainings\r\n\r\nIt is described in:\r\n\r\nBaecker V.\r\nImageJ Macro Tool Sets for Biological Image Analysis.\r\nhttp://dev.mri.cnrs.fr/documents/89\r\nIn: ImageJ User and Developer Conference 2012. Luxembourg:\r\nCentre de Recherche Public Henri Tudor; 2012; ISBN: 2-919941-18-6', 'Volker Baecker', NULL, NULL, NULL),
('2441', NULL, '138', NULL, 'quickPALM', 'QuickPALM is a set of programs to aid in the acquisition and image analysis of data in “photoactivated localization microscopy” (PALM) \r\nand “stochastic optical reconstruction microscopy” (STORM). \r\n\r\nQuickPALM features the associated QuickPALM ImageJ plugin, which enables PALM/STORM 2D/3D/4D particle detection and image reconstruction in ImageJ. ', 'Ricardo Henriques, et al', NULL, NULL, NULL),
('2445', NULL, '13', NULL, 'Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images.', NULL, NULL, NULL, NULL, NULL),
('2446', NULL, '93', NULL, 'Using CellX to quantify intracellular events.', NULL, NULL, NULL, NULL, NULL),
('2447', NULL, '139', NULL, 'Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images.', NULL, NULL, NULL, NULL, NULL),
('2449', NULL, '472', NULL, 'Moment-preserving thresolding: A new approach', 'see http://www.asia.edu.tw/Main_pages/about/Former_president_TsaiWH_publications/Journal%20Paper%20PDFs/Moment-Preserving%20Thresholding%EF%BC%9AA%20New%20Approach%28by%20Tsai,%20W%20H%29.pdf', NULL, NULL, NULL, NULL),
('2450', NULL, '426', NULL, 'Huygens Remote Manager: A Web Interface for High-Volume Batch Deconvolution', NULL, NULL, NULL, NULL, NULL),
('2141', NULL, '0', NULL, 'Huygens Remote Manager', 'The Huygens Remote Manager is an open-source, efficient, multi-user web-based interface to the Huygens software for parallel batch deconvolutions. See <bib>2450</bib>', 'Volker Bäcker, Asheesh Gulati, Alessandra Griffa, José Viña, Daniel Sevilla, Niko Ehrenfeuchter, Torsten Stöter, Aaron Ponti', NULL, 'http://huygens-rm.org/home/', NULL),
('2417', NULL, '469', NULL, 'Uneven illumination correction', '<blockquote>Illumination correction is often important for both accurate segmentation and for intensity measurements. This example shows how the CorrectIlluminationCalculate and CorrectIlluminationApply modules are used to compensate for the non-uniformities in illumination often present in microscopy images.</blockquote>', 'Tutorial under ImageJ: Gabriel Landini', NULL, NULL, NULL),
('2451', NULL, '470', NULL, 'Quantification of histochemical staining by color deconvolution.', NULL, NULL, NULL, NULL, NULL),
('2376', NULL, '470', NULL, 'Quantifying staining in tissue sections', 'The Color Deconvolution plugin for ImageJ can be used to digitally separate up to three stains from brightfield images, after which standard ImageJ commands can be used.  The algorithm is described in [bib]2451[/bib].\r\n\r\n**However**, it is **very** important to take into consideration the caveats on the linked URL.  In particular, note that:\r\n\r\n* Stain colors depend on numerous factors, such as the precise stains and scanner; therefore, the \'default\' stain vectors (used to define the colors) are unlikely to be optimal and may be very inaccurate.  See the URL instructions for how to create new stain vectors.\r\n* Pixel values should be interpreted with extreme caution; in particular, note the warning regarding \'brown\' staining that *attempting to quantify DAB intensity using this plugin is not a good idea*.\r\n\r\nNote, the pixel values provided by this plugin are 8-bit and **not** equivalent to \'optical densities\' frequently presented in the literature.\r\n\r\nColor deconvolution is particularly helpful in separating stains so that stained regions can be detected (e.g. by setting a threshold), and then the number or areas of stained structures may be quantified.  Two potential approaches would be:\r\n\r\n1) If one measurement should be made for the entire image:\r\n\r\n+ *Image -> Adjust -> Threshold...*\r\n+ *Edit -> Selection -> Create Selection*\r\n+ *Analyze -> Measure*\r\n\r\n2) If distinct structures should be measured:\r\n\r\n+ *Image -> Adjust -> Threshold...*\r\n+ *Analyze -> Analyze Particles...*', 'Gabriel Landini', NULL, NULL, NULL),
('2452', NULL, '470', NULL, 'ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67.', NULL, NULL, NULL, NULL, NULL),
('2454', NULL, '139', NULL, 'Biological imaging software tools.', NULL, NULL, NULL, NULL, NULL),
('2455', NULL, '85', NULL, 'N-cadherin and ?1-integrins cooperate during the development of the enteric nervous system.', NULL, NULL, NULL, NULL, NULL),
('2457', NULL, '444', NULL, 'Calculate object size from reference object', 'This protocol will process a complete folder of image and automatically calculate the size of top object knowing the bottom object size.', ' Stephane Dallongeville', NULL, NULL, NULL),
('2458', NULL, '139', NULL, 'Computational imaging in cell biology.', NULL, NULL, NULL, NULL, NULL),
('2459', NULL, '426', NULL, 'Image Processing and Analysis with ImageJ', 'In this workshop you will learn how to apply image analysis and processing techniques, using the public domain software ImageJ and some additions (plugins and macros). The first part explains the tools available in ImageJ while the second part is centered around image analysis and processing methods. Exercises, solutions and example images are available.', NULL, NULL, 'http://dev.mri.cnrs.fr/projects/imagej-workshop/wiki', NULL),
('2460', NULL, '470', NULL, 'SparkMaster: automated calcium spark analysis with ImageJ.', NULL, NULL, NULL, NULL, NULL),
('2438', NULL, '120', NULL, 'Tubulins', 'From the collection of reference datasets.\r\n\r\nThis sample consists to a realistic structure of 7 tubulins (diameter 25 nm) and 1 tubulin (diameter 40 nm). 100000 fluorophores are activated over 2400 frames. Tubulins I has a low level of read-out noise autofluorescence background, and Tubulins II has the same structure with higher level of noise.', NULL, NULL, NULL, NULL),
('2461', NULL, '470', NULL, 'Amplitude distribution of calcium sparks in confocal images: theory and studies with an automatic detection method.', NULL, NULL, NULL, NULL, NULL),
('2411', NULL, '470', NULL, 'Analyzing Ca2+ sparks', 'ImageJ plugin to detect and measure Ca2+ sparks in linescan images, described in [bib]2460[/bib]\r\n\r\nThe algorithm is based on that described by Cheng et al. [bib]2461[/bib].\r\n\r\nCare should be taken to ensure that detections belong to \'true\' events, as without any additional background subtraction steps the algorithm is not be appropriate for images in which the baseline fluorescence varies substantially.', 'Eckard Picht , Aleksey V. Zima , Lothar A. Blatter , Donald M. Bers', NULL, NULL, NULL),
('2456', NULL, '93', NULL, 'Cell segmentation and quantification with CellX', 'CellX is an open-source software package for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images with distinguishable cell boundary. Installation and a step-by-step usage details are described in  <bib>2446</bib>. It can be downloaded from <a href="http://www.csb.ethz.ch/tools/cellx">here</a>. After users provide a few annotations of cell sizes and cell boundary profiles, it tries to match boundary profile pattern on cells thus provide segmentation and further tracking. It works the best on cells without extreme shapes and with rather homogeneous boundary pattern. It may not work well on images with cells of sizes only a few pixels. Its output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis. ', 'Christian Mayer, Sotiris Dimopoulos, Fabian Rudolf, and Jorg Stelling', NULL, NULL, NULL),
('2463', NULL, '426', NULL, 'Why join groups? Lessons from parasite-manipulated Artemia.', NULL, NULL, NULL, NULL, NULL),
('2465', NULL, '444', NULL, 'Median filter via ImageJ', 'This protocol perform a median filter on the active sequence using the ImageJ rank filter plugin. Then, it converts the result back into Icy for display.\r\n\r\n', 'Alexandre Dufour', NULL, NULL, NULL),
('2467', '2467', '139', NULL, 'easyFRAP', 'Very simple application that lets you load your time-lapse intensity data to generate the normalized FRAP recovery curve and perform exponential curve fitting.\r\n\r\n>The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.', 'Maria Anna Rapsomaniki, Panagiotis Kotsantis, Ioanna-Eleni Symeonidou, Nickolaos-Nikiforos Giakoumakis, Stavros Taraviras and Zoi Lygerou', NULL, 'http://bioinformatics.oxfordjournals.org/content/28/13/1800.full', NULL),
('2468', NULL, '426', NULL, 'Simple system using natural mineral water for high-throughput phenotyping of Arabidopsis thaliana seedlings in liquid culture', NULL, NULL, NULL, NULL, NULL),
('2469', NULL, '469', NULL, 'Quick-and-clean article figures with FigureJ.', NULL, NULL, NULL, NULL, NULL),
('2464', NULL, '469', NULL, 'Creating image montages', 'Both tools adds to ImageJ functions to build and organize montages.\r\n\r\n\r\n<strong><a href="http://imagejdocu.tudor.lu/doku.php?id=howto:working:work_with_magic_montage">Make montage</a></strong>\r\nIt comes with the ImageJ installer but might also bee downloaded from <a href="http://imagejdocu.tudor.lu/doku.php?id=howto:working:work_with_magic_montage">the ImageJ wiki</a>.\r\n\r\n\r\n<strong><a href="http://imagejdocu.tudor.lu/doku.php?id=plugin:utilities:figurej:start"FigureJ></a></strong>\r\nCompared to Make montage, the plugins adds more flexibility to montage creation:\r\n<blockquote><ul>\r\n<li>Easy-to-design interactive figure layout.</li>\r\n<li>Visually assign image content to panels.</li>\r\n<li>High quality image scaling and rotation.</li>\r\n<li>Easy and consistent panel labels and scalebars.</li>\r\n<li>Each panel has it\'s original datasource\'s properties and tracks achieved image processing.</li>\r\n<li>Save and re-open editable figures.</li>\r\n<li>Export as standard image formats with textual description of each panel history.</li>\r\n</ul>\r\n</blockquote>\r\nDon\'t forget to cite <bib>2469</bib> when publishing using FigureJ !\r\n\r\n\r\nBoth tools come with easy to follow video tutorials. ', 'Jerome Mutterer', NULL, NULL, NULL),
('2470', NULL, '444', NULL, 'Pixel density-batch mode', 'For each ROI, provides the ratio of pixels over a given threshold over the total number of pixels in the ROI.', 'Fabrice de Chaumont', NULL, NULL, NULL),
('2472', NULL, '470', NULL, 'ImmunoMembrane: a publicly available web application for digital image analysis of HER2 immunohistochemistry.', NULL, NULL, NULL, NULL, NULL),
('2471', NULL, '426', NULL, 'ImageJ Macro Tool Sets for Biological Image Analysis', NULL, NULL, NULL, NULL, NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2475', NULL, '470', NULL, 'Quantification of HER2 immunohistochemistry', 'ImageJ plugin for assessing HER2 immunohistochemistry, described in [bib]2472[/bib].\r\n\r\nIt is important to read the URL documentation and original paper to understand how to use the plugin appropriately.\r\n\r\nNote also that the *pixel size* is not read automatically from the image, but rather the *source image scale* should be entered into the dialog box - and the image rescaled accordingly prior to analysis. This scale value is the *inverse* of the value normally found for *pixel width* and *pixel height* under *Image -> Properties...* (i.e. pixel width & height are given in microns per pixel; the dialog box asks for pixels per micron).', 'Vilppu Tuominen & Jorma Isola', NULL, NULL, NULL),
('2374', NULL, '426', NULL, 'Adipocyte quantification', 'The Adipocytes Tools help to analyze fat cells in images from histological section. This is a rather general cell segmentation approach. It can be adapted to different situations via the parameters. This means that you have to find the right parameters for your application. \r\n\r\nIt is described in <bib>2471</bib>', 'Volker Baecker', NULL, NULL, NULL),
('2479', NULL, '92', NULL, 'Robust single-particle tracking in live-cell time-lapse sequences.', NULL, NULL, NULL, NULL, NULL),
('2480', NULL, '469', NULL, 'Automated cell tracking and analysis in phase-contrast videos (iTrack4U): development of Java software based on combined mean-shift processes.', NULL, NULL, NULL, NULL, NULL),
('2431', NULL, '426', NULL, 'Skin Tools', 'The skin tools measure the thickness of the epidermis and the interdigitation index. The input images are masks that represent the epidermis and that have been created from images of stained histological sections. The mask must touch the left and right border of the image. The dermal-epidermal border must be on the lower site of the image. The interdigitation index can be measured for one or more segments per image. As a measure of the thickness of the epidermis the lengths of a number of random line segments are measured. The line segments start at the lower border, are perpendicular to the lower border and end at the opposite border of the mask. See <bib>2471</bib>.\r\n', 'Volker Baecker', NULL, NULL, NULL),
('2482', NULL, '470', NULL, 'A method for normalizing histology slides for quantitative analysis', NULL, NULL, NULL, NULL, NULL),
('2437', NULL, '426', NULL, 'Wound Healing Tool', 'The wound healing tool measures the area of a wound in a time series of images of cellular tissue. The tool will measure the area of the wound, i.e. the area that does not contain tissue, in each image. The segmentation is based on the fact that the image is more homogeneous in the region of the wound as in the region of the tissue. Via the options, one of two methods to detect the empty area, can be selected. The first uses edge detection, the second a variance filter. Holes in the detected tissue are filled using morphological operations. See <bib>2471</bib>\r\n', 'Volker Baecker', NULL, NULL, NULL),
('2483', NULL, '469', NULL, 'Automated cell tracking and analysis in phase-contrast microscopy', 'iTrack4U is a Java-based software using ImageJ and jMathPlot libraries, which aims at automatically tracking cells recorded in phase-contrast microscopy. It includes all tools from image files preprocessing, tracking to data extraction and visualization. \r\n\r\n\r\nPlease cite <bib>2480</bib> when using this software package !', 'Fabrice P. Cordelières', NULL, NULL, NULL),
('2487', NULL, '13', NULL, 'IDL', 'IDL, short for Interactive Data Language, is a programming language used for data analysis.\r\nIt is popular in particular areas of science, such as astronomy and medical imaging.\r\n\r\nIDL is vectorized, numerical, and interactive, and is commonly used for interactive processing of large amounts of data (including image processing). \r\nThe syntax includes many constructs from Fortran and some from C.', NULL, NULL, 'http://www.exelisvis.com/ProductsServices/IDL.aspx', NULL),
('2488', NULL, '85', NULL, 'testcomponenet', NULL, NULL, NULL, NULL, NULL),
('2489', NULL, '92', NULL, 'A PAR-1-dependent orientation gradient of dynamic microtubules directs posterior cargo transport in the Drosophila oocyte.', NULL, NULL, NULL, NULL, NULL),
('2401', NULL, '426', NULL, 'Artemia color analysis', 'The Artemia Tools help to calculate the normalized redness of Artemia in color images. \r\n\r\nIt has been used in <bib>2463</bib>', 'Volker Baecker', NULL, NULL, NULL),
('2364', NULL, '92', NULL, 'Microtubule end tracking', 'Microtubule end tracking in live cell fluorescent images (e.g. to characterise organisation of the cytoskeleton) involves overcoming the following challenges, which can be tackled by a series of filtering steps <bib>2489</bib>:\r\n\r\n* **illumination flicker & photobleaching**: suppress by normalising intensities, e.g. using Image->Adjust->Bleach Correction in Fiji/ImageJ\r\n* **uneven illumination**: Fourier bandpass filtering (e.g. Process->FFT->Bandpass Filter) preserves features within a selected size range\r\n* **high background / poor contrast**: foreground filter, e.g. Temporal Median filter\r\n* **tracking**: e.g. TrackMate in Fiji/ImageJ (segmentation using DoG detector)', 'Graeme Ball', NULL, NULL, NULL),
('2492', NULL, '13', NULL, 'Wolfram Language', 'The Wolfram Language is the programming language used in Mathematica.\r\nMathematica is a computational software program used in many scientific, engineering, mathematical and computing fields, based on symbolic mathematics.\r\nThe feature list is long and includes tools for 2D and 3D image processing and morphological image processing including image recognition\r\n\r\nFrom Wikipedia:\r\nThe Wolfram Language (popularly referred to as Mathematica, or simply M) is a general multi-paradigm programming language developed by Wolfram Research, that serves as the main interfacing language for Mathematica and the Wolfram Programming Cloud. It is designed to be as general as possible, with emphasis on symbolic computation, functional programming, and rule-based programming. It is built to represent arbitrary structures and data.', NULL, NULL, 'http://www.wolfram.com/language/', NULL),
('2486', NULL, '470', NULL, 'Color normalization of H&E stains', 'The overall colors seen in H&E stained slides can vary widely, influenced by factors such as the precise stains and scanner used.  This MATLAB function implements the color normalization strategy described in [bib]2482[/bib] in order to match stain colors in an image more closely to \'reference\' stains.  This may help when comparing images visually, or when applying an automated analysis algorithm.\r\n\r\nThe function may also be useful to understand the functioning of the color deconvolution described in [bib]2451[/bib].', 'Mitko Veta', NULL, NULL, NULL),
('2094', NULL, '0', NULL, 'Maximum Entropy Threshold', 'This plugin threshold an image using the Maximum Entropy algorithm, which aims at maximizing the inter-class entropy.\r\n\r\nEntropy is defined as -sum(p.*log2(p)), where p contains the histogram bin counts.\r\n\r\nThis thresholding is very useful to segment images with few bright objects on large dark background.\r\n\r\nIn ImageJ/FIJI you can acces this tool in Image->Adjust->Threshold and choose in the list\r\n\r\nIn Aphelion, you can access this tool in Seglmentation->Threshold-> AphImgEntropyThreshold\r\n\r\n', NULL, NULL, 'http://fiji.sc/Maximum_Entropy_Threshold', NULL),
('2436', NULL, '472', NULL, 'Moment thresholding', 'This method allows to compute a threshold that preserves the moments of an image.\r\n\r\nIn ImageJ/Fiji, you can access it in Image->Ajust->Threshold and choose Moments in the list.\r\n\r\nIn Aphelion, the tool is in Segmentation->Threshold->AphImgMomentThreshold\r\n\r\nThe original paper is <bib>2449</bib>', 'W. Tsai', NULL, 'http://fiji.sc/Auto_Threshold#Moments', NULL),
('2494', NULL, '75', NULL, '2-D Coloalisation in Cells', 'The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. \r\nPlease read here for more information: https://github.com/tischi/ImageAnalysisWorkflows/blob/master/CellProfiler_2D_Coloc_PerCell/README.md', 'Christian "Tischi" Tischer', NULL, NULL, NULL),
('2495', NULL, '139', NULL, 'Analysis of  Focal Adhesion Spatiotemporal Dynamics', 'The publication describes a quantitative analysis of focal adhesion dynamics that have been imaged using TIRF. All image processing steps are well explained or referenced.\r\n\r\n>To better understand the dynamic regulation of focal adhesions, we have developed an analysis system for the automated detection, tracking, and data extraction of these structures in living cells. This analysis system was used to quantify the dynamics of fluorescently tagged Paxillin and FAK in NIH 3T3 fibroblasts followed via Total Internal Reflection Fluorescence Microscopy (TIRF). High content time series included the size, shape, intensity, and position of every adhesion present in a living cell. These properties were followed over time, revealing adhesion lifetime and turnover rates, and segregation of properties into distinct zones. ', 'Mathew E. Berginski, Eric A. Vitriol, Klaus M. Hahn, Shawn M. Gomez', NULL, NULL, NULL),
('2496', NULL, '120', NULL, 'PALMsiever: a tool to turn raw data into results for single-molecule localization microscopy.', NULL, NULL, NULL, NULL, NULL),
('2498', NULL, '94', NULL, 'Drosophila embryo epithelium', 'Drosophila embryo epithelium expressing mCherry-cadherin, a cell junction label.', NULL, NULL, NULL, NULL),
('2497', NULL, '11', NULL, '3D intensity profile', 'This Javascript works in ImageJ to measure 3D intensity profile along cylindrical space with variable radius. ', 'Kota Miura', NULL, NULL, NULL),
('2453', NULL, '467', NULL, 'Find and Draw Wells in a Multi-Well Plate Picture', 'This macro recognizes wells in a picture from a multi-well plate (it works also on a picture of a single well). It is used to segment a picture to determine the number of "Colony Forming Units" in each individual well of a plate.\r\nThe steps are the following:\r\n1. Makes a 8-bit B&W picture, inverts it (=> borders will look white instead of black), resizes it (optional, this is to speed-up convolution thereafter) and find edges.\r\n2. Convolves the obtained picture with a kernel corresponding to a thick white circle of the size of the wells. The resulting image has big "blobs" or "particles" corresponding roughly to the centers of the well.\r\n3. The image is thresholded to remove particles not corresponding to strong hits and "Analyze particle" is run.\r\n4. The measured parameter is the center of mass of the particles wich gives the center of the well. These are saved in an array.\r\n5. Circles are drawn and added to the ROI manager. The centers of the circles are the identified centers of mass of the particles and their radius is the expected radius of the wells in the original image.', 'Laurent Gelman', NULL, NULL, NULL),
('2477', NULL, '444', NULL, 'Analyze Skeleton', 'This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length. The tags are shown in a new window displaying every tag in a different color. You can find it under Plugins>Skeleton>Analyze Skeleton (2D/3D). See Skeletonize3D for an example of how to produce skeleton images.\r\n\r\nThe voxels are classified into three different categories depending on their 26 neighbors:\r\n\r\n    End-point voxels: if they have less than 2 neighbors.\r\n    Junction voxels: if they have more than 2 neighbors.\r\n    Slab voxels: if they have exactly 2 neighbors.\r\n\r\nEnd-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.\r\n\r\nNotice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other. ', 'Ignacio Arganda-Carreras', NULL, NULL, NULL),
('2501', NULL, '426', NULL, 'Arabidopsis plants acclimate to water deficit at low cost through changes of carbon usage: an integrated perspective using growth, metabolite, enzyme, and gene expression analysis', NULL, NULL, NULL, NULL, NULL),
('2500', NULL, '93', NULL, 'Detect Cells with MATLAB CellDetector', 'CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets.\r\nThe installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.\r\nhttp://www.robots.ox.ac.uk/~vgg/software/cell_detection/', 'Carlos Arteta, Victor Lempitsky, Alison Noble, and Andrew Zisserman', NULL, NULL, NULL),
('2502', NULL, '93', NULL, 'Towards real-time image deconvolution: application to confocal and STED microscopy.', NULL, NULL, NULL, NULL, NULL),
('2503', NULL, '93', NULL, 'Deconvolution applicable to confocal and STED microscopy', 'This Matlab function implements the SGP method for n-dimensional object deblurring with the option of boundary effects removal. Although this is a preliminary version, results seem to be good from their paper <bib>2502</bib>.', 'Zanella R, Zanghirati G, Cavicchioli R, Zanni L, Boccacci P, Bertero M, Vicidomini G.  ', NULL, NULL, NULL),
('2359', NULL, '94', NULL, 'Tissue Cell Segmentation', 'This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).', 'Sébastien Tosi', NULL, NULL, NULL),
('2506', NULL, '93', NULL, 'Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh.', NULL, NULL, NULL, NULL, NULL),
('2508', NULL, '94', NULL, 'Splitting clustered cells', 'A clear tutorial on a simple workflow for splitting connected particles in a binary mask.', 'Steve Eddins', NULL, NULL, NULL),
('2504', NULL, '11', NULL, 'FISHfinder', 'Quote: "By posing the nucleus extraction as a classification problem, compound Bayesian Classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters."', 'Gilbert@bio.fsu.edu', NULL, NULL, NULL),
('2509', NULL, '470', NULL, 'Automated detection and measurement of isolated retinal arterioles by a combination of edge enhancement and cost analysis.', NULL, NULL, NULL, NULL, NULL),
('2511', NULL, '426', NULL, 'Measure Rosette Area Tool', 'The Measure Rosette Area Tool allows to measure the area of the rosettes of arabidopsis plants. The workflow has been used in <bib>2501</bib>.', 'Volker Baecker', NULL, NULL, NULL),
('2513', NULL, '11', NULL, 'FISH-quant: automatic counting of transcripts in 3D FISH images.', NULL, NULL, NULL, NULL, NULL),
('2499', NULL, '85', NULL, 'Texture Analyzer', 'This plugin will return on a full 256-grey level image (limitation in this version) or on a ROI several texture features such as described in Haralick publication (bib ref to be added).\r\nCan be run in batch mode.', 'Julio E. Cabrera NIH', NULL, 'http://rsbweb.nih.gov/ij/plugins/texture.html', NULL),
('2520', NULL, '120', NULL, 'rapidSTORM: accurate, fast open-source software for localization microscopy.', NULL, NULL, NULL, NULL, NULL),
('2521', NULL, '467', NULL, 'PixFRET, an ImageJ plug-in for FRET calculation that can accommodate variations in spectral bleed-throughs.', NULL, NULL, NULL, NULL, NULL),
('2522', NULL, '94', NULL, 'Intelligent Imaging', 'An ImageJ macro able to control some microscopes (Micro-manager or Leica CAM controlled) to acquire high resolution images of only some structures (e.g. isolated cells) or events (e.g. mitosis) within a sample. The scan is sequenced as a primary (low resolution monitoring) scan and a secondary (high resolution, multi-dimensional) scan.', 'Sébastien Tosi', NULL, NULL, NULL),
('2516', NULL, '467', NULL, 'Sensitized Emission FRET calculation on pictures', 'The ImageJ pligin, called PixFRET, allows a simple and rapid determination of channel bleed-through parameters and the display of normalized FRET images. see <bib>2521</bib>', 'Laurent Gelman', NULL, NULL, NULL),
('2514', NULL, '94', NULL, 'CytooChip Analysis', 'This macro is a plugin macro to the "Intelligent Imaging" workflow. It detects the Cytoo patterns (specific fluorsecence channel) and computes the occupancy (number of cells) of each pattern by analyzing the images of the DAPI channel. The analysis function can be easily extended to, for instance, only select the cells that are well spread on the patterns (by analyzing a third channel with a properly chosen marker of the cytoplasm).', 'Sébastien', NULL, NULL, NULL),
('2518', '2518', '120', NULL, 'RapidSTORM', 'The rapidSTORM project is an open source evaluation tool that provides fast and highly configurable data processing for single-molecule localization microscopy such as dSTORM. It provides both two-dimensional and three-dimensional, multi-color data analysis as well as a wide range of filtering and image generation capabilities. The general operation of rapidSTORM is described in this article<bib>2520</bib>.\r\n', 'Steve Wolter, Sven Proppert, Sarah Aufmkolk, André Lampe, Teresa Klein', NULL, 'http://www.super-resolution.biozentrum.uni-wuerzburg.de/research_topics/rapidstorm/', NULL),
('2263', '2263', '2', NULL, 'PALMsiever', 'PALMsiever is a MATLAB-based application that allows the filtering (sieving) and analysis of localization-microscopy data. It provides the ability to render the data using different visualization algorithms and perform simple measurements on the point-localization data. It is extensible using simple MATLAB scripts and a number of plugins is already provided with the software itself, including a clustering algorithm and 3D rendering <bib>2496</bib>.\r\n\r\nStrengths: intuitive, easy navigation through the point-localization data\r\nLimitations: no multi-color\r\n', 'Thomas Pengo, Seamus Holden', NULL, 'https://code.google.com/p/palm-siever/', NULL),
('2524', NULL, '93', NULL, 'Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh', 'This Fiji plugin, Squassh, its workflow and protocol is described in <bib>2506</bib>. It enables detecting, segmenting, and quantifying subcellular structures in fluorescence 2D/3D microscopy images. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time lapse data. A brief summary note can be found <a href="http://cellnetmcweb.bioquant.uni-heidelberg.de/image-analysis/ShortTutorials/Squassh.pdf">here</a>.\r\n', 'Rizk A, Paul G, Incardona P, Bugarski M, Mansouri M, Niemann A, Ziegler U, Berger P, Sbalzarini IF', NULL, NULL, NULL),
('2525', NULL, '94', NULL, 'Cell segmentation', 'A clear tutorial on how to build a simple workflow to segment clustered cells in Matlab.', ' Steve Eddins', NULL, NULL, NULL),
('2528', NULL, '11', NULL, 'TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization.', NULL, NULL, NULL, NULL, NULL),
('2534', NULL, '120', NULL, 'FtsZ-Dendra2 C. crescentus live', 'FtsZ-Dendra2 C crescentus live 3D 10 ms frame', NULL, NULL, NULL, NULL),
('2533', NULL, '444', NULL, 'Variational algorithms to remove stationary noise', 'Variational algorithms to remove stationary noise. Application to microscopy imaging.\r\n\r\nThis plugin allows to denoise images degraded with stationary noise. Stationary\r\nnoise can be seen as a generalization of the standard white noise. Typical\r\napplications of this plugin are:\r\n               - Standard white noise denoising using a total variation and fidelity term\r\nminimization. Figure 1 is an example of this application. Even though\r\ntotal variation denoising is not the state of the art (regarding SNR im-\r\nprovement), it may be very valuable for further tasks such as image seg-\r\nmentation).\r\n               - Destriping (the problem that motivated us to develop these ideas). Figures\r\n2, 3 and 4 are examples of this application.\r\n               - Deconvolution (even though most users won\'t be able to use this feature).\r\n               - Cartoon + texture decomposition which might be useful to compress im-\r\nages, analyse textures or simplify segmentation like tasks.', 'J. Fehrenbach, P. Weiss and C. Lorenzo,', NULL, 'http://www.math.univ-toulouse.fr/~weiss/PageCodes.html', NULL),
('2537', NULL, '11', NULL, '3D affine transformation based on paired points', 'Using a text file containing 3D point coordinates as reference pairs, 3D image stack is transformed. ', 'Kota Miura', NULL, NULL, NULL),
('2540', NULL, '470', NULL, 'Measurement of Isolated Retinal Arterioles', 'ImageJ plugin to analyze changes in vessel diameters, described in [bib]2509[/bib]', 'Jose Fernandez', NULL, NULL, NULL),
('2542', NULL, '120', NULL, 'Tubulin Conj-AL647', 'Experimental sequence of 27\'529 frames (128x128 pixels) with the parameters of acquisition.\r\nThe dataset consists in 27\'529 frames of 128x128 pixels stored as stack TIFF in 16 bits.\r\nBe aware that it is about 1Gb of data.', NULL, NULL, NULL, NULL),
('2543', NULL, '120', NULL, 'Tubulins - High Density', 'Real dataset\r\nLong sequence settings, 15000 frames\r\nCourtesy of Nicolas Olivier, Debora Keller and Suliana Manley\r\n', NULL, NULL, NULL, NULL),
('2326', '2326', '11', NULL, 'Huygens', '-', NULL, NULL, 'http://www.svi.nl/HomePage', NULL),
('2545', NULL, '426', NULL, 'Automated whole animal bio-imaging assay for human cancer dissemination.', NULL, NULL, NULL, NULL, NULL),
('2546', NULL, '139', NULL, 'Worm Toolbox: High.throughput screening of C. elegans', '>..A toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.', 'Carolina Wählby,	Lee Kamentsky, Zihan H Liu, Tammy Riklin-Raviv, Annie L Conery, Eyleen J O\'Rourke, Katherine L Sokolnicki, Orane Visvikis,	Vebjorn Ljosa, Javier E Irazoqui, Polina Golland, Gary Ruvkun,	Frederick M Ausubel & Anne E Carpenter', NULL, NULL, NULL),
('2420', '2420', '139', NULL, 'Ctrax - Caltech Multiple Fly Tracker', 'Well maintained and documented project that includes a core tracking incl. GUI as well as Matlab toolboxes to (1) correct tracking results and (2) analyze fly behavior.\r\n\r\n>Ctrax is an open-source, freely available, machine vision program for estimating the positions and orientations of many walking flies, maintaining their individual identities over long periods of time. It was designed to allow high-throughput, quantitative analysis of behavior in freely moving flies. Our primary goal in this project is to provide quantitative behavior analysis tools to the neuroethology community, thus we\'ve endeavored to make the system adaptable to other labs\' setups. We have assessed the quality of the tracking results for our setup, and found that it can maintain fly identities indefinitely with minimal supervision, and on average for 1.5 fly-hours automatically.', 'Kristin Branson, Alice A Robie, John Bender, Pietro Perona & Michael H Dickinson', NULL, 'http://ctrax.sourceforge.net/', NULL),
('2547', NULL, '94', NULL, 'Cell segmentation in phase contrast images', 'This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.', 'Chunming Li', NULL, NULL, NULL),
('2548', NULL, '139', NULL, 'Objective comparison of particle tracking methods.', NULL, NULL, NULL, NULL, NULL),
('2549', NULL, '139', NULL, 'Fluorophore localization algorithms for super-resolution microscopy.', NULL, NULL, NULL, NULL, NULL),
('2550', NULL, '426', NULL, 'Zebrafish Embryo Tool', 'Normalize the orientation of the images of the Zebrafish embryos. ImageJ macro implementation of the Workflow described in <bib>2545</bib>. Note that currently only the angle and orientation normalization is implemented in this version.', 'Volker Baecker', NULL, NULL, NULL),
('2551', NULL, '139', NULL, 'Software opens the door to quantitative imaging.', NULL, NULL, NULL, NULL, NULL),
('2553', NULL, '94', NULL, 'Image Segmentation Tutorial', NULL, 'A Great Guy', NULL, NULL, NULL),
('2556', NULL, '120', NULL, 'TetraSpeck bead drift example', 'TetraSpeck bead drift example 30ms frame analyzed by RapidSTORM.', NULL, NULL, NULL, NULL),
('2552', '2552', '13', NULL, 'MountainsMap', 'MountainsMap is a surface imaging and metrology software published by the company Digital Surf. \r\nIts main application is micro-topography, the science of studying surface texture and form in 3D at the microscopic scale. \r\nThe software is used mainly with stylus-based or optical profilometers, optical microscopes and scanning probe microscopes.\r\n\r\nMountainsMap is mainly offered as embedded or optional OEM analysis software by most profilometer and microscope manufacturers, usually under their respective brands; it is sold for instance as:\r\n  MountainsMap - X on Nikon\'s microscopes\r\n  Leica Map on Leica\'s microscopes\r\n  ConfoMap on Carl Zeiss\' microscopes', 'Digital Surf', NULL, 'http://www.digitalsurf.fr/en/mntkey.html', NULL),
('2557', NULL, '94', NULL, 'DNA MicroArray Image Processing Case Study', 'In this case study, MATLAB, the Image Processing and Signal Processing toolboxes were used to determine the green intensities from a small portion of a microarray image containing 4,800 spots. A 10x10 pattern of spots was detected by averaging rows and columns to produce horizontal and vertical profiles. Periodicity was determined automatically by autocorrelation and used to form an optimal length filter for morphological background removal. A rectangular grid of bounding boxes was defined. Each spot was individually addressed and segmented by thresholding to form a mask. The mask was used to isolate each spot from surrounding background. Individual spot intensity was determined by integrating pixel intensities. Finally, integrated intensities were tabulated and saved to a data file for subsequent statistical analysis to determine which genes matter most.', 'Robert Bemis', NULL, NULL, NULL),
('2558', NULL, '471', NULL, 'Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP', NULL, NULL, NULL, NULL, NULL),
('2512', NULL, '120', NULL, 'Evaluate 3D data with a bead calibration sample', NULL, 'RapidSTORM authors', NULL, NULL, NULL),
('2555', NULL, '471', NULL, 'Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis with TIMP', 'Complete workflow for fluorescence lifetime analysis as an R package with sample data.\r\n\r\n<bib>2558</bib>', 'Sergey Laptenok', NULL, NULL, NULL),
('2559', NULL, '11', NULL, 'Kymoquant', 'Measurement of kymograph generally deals with slanted streaks in kymograph to measure velocity of movements. This is a pretty much manual procedure that needs hands-on work. Kymoquant analyzes kymograph by treating patterns appearing in kymograph as texture: for a specified area in kymograph, it detects the most likely orientation of streaks and outputs this result as velocity. \r\n\r\nThis workflow was created to ease the situation when it is difficult to find major direction of streaks only with eyes. \r\n\r\n', 'Kota Miura', NULL, NULL, NULL),
('2561', '2561', '94', NULL, 'Phenoripper', 'An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.', 'Satwik Rajaram,	Benjamin Pavie,	Lani F Wu	& Steven J Altschuler', NULL, 'https://github.com/AltschulerWu-Lab/phenoripper', NULL),
('2562', NULL, '11', NULL, 'Microtubule dynamics reconstituted in vitro and imaged by single-molecule fluorescence microscopy.', NULL, NULL, NULL, NULL, NULL),
('2563', NULL, '426', NULL, 'Colony Blob Count Tool', 'Count bacterial colonies on agar plates and measure the occupied surfaces. The user has to provide a selection (roi) of the area that will be analyzed. He can than run the segmentation and if necessary correct the results. In a third step he can run the counting and measurement.', 'Volker Baecker', NULL, NULL, NULL),
('2527', NULL, '120', NULL, 'Drift correction using PALMsiever', NULL, 'Seamus Holden', NULL, NULL, NULL),
('2567', NULL, '11', NULL, 'SLOTH', 'Microtubule tracking, written in Python <bib>2562</bib>.', 'Gell C, Bormuth V, Brouhard GJ, Cohen DN, Diez S, Friel CT, Helenius J, Nitzsche B, Petzold H, Ribbe J, Schäffer E, Stear JH, Trushko A, Varga V, Widlund PO, Zanic M, Howard J', NULL, NULL, NULL),
('2564', '2564', '13', NULL, 'Visiopharm', 'The software is designed for pathologists. \r\nImage analysis protocols are built from graphical user interfaces; there is no need for programming experience or an extensive training program.\r\n\r\nCloud and deployed solutions are available.\r\nVisiopharm can be employed to develop workflows (apps) for the user.\r\n\r\nModular structure with multiple packages:\r\nVisiomorphDP™		\r\nTissuemorphDP™		\r\nArrayimager™		\r\nTissuealign™		\r\nVisiomorph™		\r\nTissuemorph™		\r\nMicroimager™		\r\nFluoimager™', 'Visiopharm', NULL, 'http://www.visiopharm.com', NULL),
('2466', NULL, '85', NULL, 'Tissue Texture analysis', 'Still in edition. Creating relative components and sample data.\r\n\r\nThe workflow was used in <bib>2455</bib>.', 'A Great Guy', NULL, NULL, NULL),
('2568', NULL, '426', NULL, 'Root tools', 'The root tools help to efficiently measure the following characteristics of plant roots:\r\n    the angle of the opening of the whole root\r\n    the depth to which it goes down\r\n    the number of roots at multiple depths (for example 30cm, 35cm, ...)\r\n    the diameters of the roots at multiple depths (for example 30cm, 35cm, ...)\r\n', 'Volker Baecker', NULL, NULL, NULL),
('2529', NULL, '120', NULL, 'DBSCAN clustering using PALMsiever', 'DBSCAN (Density-based spatial clustering of applications with noise) performs multi-dimensional clustering based on the local density of points. This plugin is implemented for 2-3 dimensions.', 'Seamus Holden', NULL, NULL, NULL),
('2274', '2274', '2', NULL, 'Imaris', 'Imaris is a software for data visualization, analysis, segmentation and interpretation of 3D and 4D microscopy images. It performs interactive volume rendering that lets users freely navigate even very large datasets (hundreds of GB). It performs both manual and automated detection and tracking of biological “objects” such as cells, nuclei, vesicles, neurons, and many more. ImarisSpots for example is a tool to detect “spherical objects” and track them in time series. Besides the automated detection it gives the user the ability to manually delete and place new spots in 3D space. ImarisCell is a tool to detect nuclei, cell boundaries and vesicles and track these through time. ImarisFilament is a module that lets users trace neurons and detect spines. For any detected object Imaris computes a large set of statistics values such as volume, surface area, maximum intensity of first channel, number of vesicles per cell etc. These values can be exported to Excel and statistics software packages. The measurements can also be analyzed directly within ImarisVantage which is a statistics tool that provides the link back to the 3D objects and the original image data.\r\n\r\nStrengths: \r\n- good visualization\r\n- user friendly interface\r\n- reads most microscopy file formats\r\n- image analysis workflows are very easy to apply\r\n- interactive editing of objects to correct errors during automatic detection\r\n- large data visualization (hundreds of GB)\r\n', 'Bitplane', NULL, 'http://www.bitplane.com/', NULL),
('2303', '2303', '11', NULL, 'Aquiarium', 'Acquiarium is for carrying out the common pipeline of many spatial cell studies using fluorescence microscopy. It addresses image capture, raw image correction, image segmentation, quantification of segmented objects and their spatial arrangement, volume rendering, and statistical evaluation.\r\n\r\nIt is focused on quantification of spatial properties of many objects and their mutual spatial relations in a collection of many 3D images.  It can be used for analysis of a collection of 2D images or time lapse series of 2D or 3D images as well. It has a modular design and is extensible via plug-ins. It is a stand-alone, easy to install application written in C++ language. The GUI is written using cross-platform wxWidgets library.', NULL, NULL, 'http://cbia.fi.muni.cz/projects/acquiarium.html', NULL),
('2569', NULL, '11', NULL, 'Distinctive Image Features from Scale-Invariant Keypoints', NULL, NULL, NULL, NULL, NULL),
('2570', NULL, '11', NULL, 'Multi-image matching using multi-scale oriented patches', NULL, NULL, NULL, NULL, NULL),
('2519', NULL, '444', NULL, 'Register Virtual Stack Slices', 'A Jython script using the plugin : Register Virtual Stack Slices\r\n\r\nIt takes a sequence of image slices stored in a folder, and delivers a list of registered image slices (with enlarged canvas). One of the images in the sequence can be selected by the user as reference and it will remain intact.\r\n\r\nThe plugin can perform 6 types of image registration techniques:\r\n\r\n- Translation\r\n- Rigid (translation + rotation)\r\n- Similarity (translation + rotation + isotropic scaling)\r\n- Affine\r\n- Elastic (via bUnwarpJ with cubic B-splines)\r\n- Moving least squares\r\n\r\nAll models are aided by automatically extracted SIFT features. \r\n', 'Albert Cardona, Ignacio Arganda-Carreras and Stephan Saalfeld', NULL, NULL, NULL),
('2295', NULL, '11', NULL, 'Manual Frame Drift Registration', '<h3>An ImageJ macro for correcting frame drift occurred during image acqusition.</h3>\r\n\r\n<p>It often happens that you have an image sequence that shows problematic drifting of image frame and at the same time you have some landmarks that could be used for correcting the drift.</p>\r\n\r\n<p>This ImageJ macro allows you to</p>\r\n\r\n<ol>\r\n<li>Manually track the landmark using <a href="http://biii.info/node/1579">ImageJ Manual Tracking Plugin</a>.</li>\r\n<li>Using the coordinates recorded in the Result window, each frame is shifted back so that the landmark stays in a single place.</li>\r\n</ol>\r\n', 'Kota Miura', NULL, NULL, NULL),
('2301', NULL, '11', NULL, 'BioImage Information Index', 'Biii is a website for organizing bioimage analysis resources. We **manually** edit following pages, for you to search tools, workflows and functions for bioimage analysis: \r\n\r\n- [Component page](http://biii.info/function): The implementation of image processing and analysis algorithms.\r\n- [Workflow page](http://biii.info/workflows): Image analysis workflow (a sequence of components) for biological research. \r\n- [Software/Library page](http://biii.info/software-table): A package of various components. \r\n- [Sample Data page](http://biii.info/workflows): Sample image data for testing workflows and components. \r\n\r\nPlease enjoy!\r\n\r\n---\r\n\r\n---', NULL, NULL, NULL, NULL),
('2281', NULL, '11', NULL, 'About', 'Each component and workflow pages are tagged manually with biological and/or image processing term, to develop mutual and organic links between biological and image processing regimes. For more about the aim of this site, please read [About](http://biii.info/node/2281) and [EuBIAS Manifesto](http://biii.info/node/2261).\r\n\r\nWe started constructing **Biii** on Oct. 11, 2013 during [the first taggathon in Barcelona](http://eubias2013.irbbarcelona.org/taggathon). We made major upgrades and added ca 140 pages of workflows in [the second taggathon in Paris in Dec. 2014](http://eubias.org/eubias2015/?page_id=52). \r\n\r\n#What BIII can do for you, what you can do for BIII\r\n\r\n<p>Existing software packages afford a rich range of tools for processing and analysis of microscope images. They are constantly evolving and the number of functions is steadily growing. With so many choices offered to the users, the problem to choose the best tool to answer a specific question is not trivial and is sometimes extremely complex. There is a gap between the rich availability of tools and the users. A simple list of links to software packages does not help much.</p>\r\n\r\n<p>We need to have a bridge. Biii (BioImage Information Index) is this bridge, allowing you to search for a tool you need for your bioimage analysis. At the same time you could contribute to this site by adding functions and packages, tagging functions, commenting and evaluating tools.</p>\r\n\r\n<p>Biii will continue to evolve, enhancing its usability and its contents.</p>\r\n\r\n<p>For more details, please see "The webtool" section in the <a href="http://biii.info/node/2261">EuBIAS Manifesto</a>.</p>\r\n\r\n<h1>Taggers</h1>\r\n\r\n<p>The following "taggers" attended the first taggathon in EuBIAS, Oct 9 - 11, 2013 and created this site:</p>\r\n\r\n<ul>\r\n<li>Jason Swedlow, Univ. Dundee &amp; EuroBioimaging WP11</li>\r\n<li>Kota  Miura, EMBL Heidelberg</li>\r\n<li>Julien Colombelli, IRB Barcelona</li>\r\n<li>Sébastien Tosi, IRB Barcelona</li>\r\n<li>Perrine Paul-Gilloteaux, Curie, Paris</li>\r\n<li>Christian Tischer, EMBL Heidelberg</li>\r\n<li>Christoph, Moehl, DZNE Bonn</li>\r\n<li>Thomas Pengo, CRG Barcelona</li>\r\n<li>Simon F Nørrelykke, ETH Zurich</li>\r\n<li>Carlos Ortiz de Solórzano Aurusa, CIMA, Pamplona</li>\r\n<li>Petr Walczysko, Univ. Dundee (OME)</li>\r\n<li>Ricard Delgado Gonzalo, BIG - EPFL</li>\r\n<li>Thomas Walter, Mines Paris</li>\r\n<li>Johannes Schindelin, LOCI, Univ. Madison (Fiji / ImageJ)</li>\r\n<li>Fabrice de Chaumont, Pasteur, Paris (ICY)</li>\r\n<li>Martin Horn, Univ. Konstanz (KNIME)</li>\r\n<li>Lee Kamentsky, Broad Institute, Boston (CellProfiler)</li>\r\n<li>Christoph Sommer, IMBA Vienna (CellCognition)</li>\r\n<li>Ullrich Kloethe, U. Heidelberg (VIGRA)</li>\r\n<li>Nicolas Rey-Villamizar, Houston (Farsight)</li>\r\n<li>Laszlo Marak, ESIEE (PINK)</li>\r\n<li>Stuart Berg, Janelia Farm, HHMI (Ilastik)</li>\r\n<li>Luis Pedro Coelho, EMBL Heidelberg (Mahotas)</li>\r\n<li>Joe Barry, EMBL Heidelberg (EBImage)</li>\r\n<li>Peter Majer, Bitplane (Imaris)</li>\r\n<li>Ronald Ligteringen, Delft University of Technology (DIPimage)</li>\r\n<li>Fabrice Cordelières, Bordeaux Imaging Center</li>\r\n<li>Ofra Golani, Weizmann, Rehovot</li>\r\n<li>Chong Zhang, CellNetworks, Univ. Heidelberg</li>\r\n<li>Nikolay Kladt, CECAD, Koeln</li>\r\n<li>Graeme Ball, Univ. Dundee</li>\r\n</ul>\r\n\r\n<p>The following "taggers" attended <a href="http://eubias.org/eubias2015/?page_id=52">the second EuBIAS taggathon, Dec 8 - 9, 2014</a>,  and worked on upgrading the web functinality and adding 140 workflows:</p>\r\n\r\n<ul>\r\n<li>Perrine Paul-Gilloteaux, Institut Curie, Paris</li>\r\n<li>Kota  Miura, EMBL Heidelberg / NINS Tokyo</li>\r\n<li>Julien Colombelli, IRB Barcelona</li>\r\n<li>Sébastien Tosi, IRB Barcelona</li>\r\n<li>Christoph, Moehl, DZNE Bonn</li>\r\n<li>Thomas Pengo, Univ. Minnesota</li>\r\n<li>Simon F Nørrelykke, ETH Zurich</li>\r\n<li>Fabrice Cordelières, Bordeaux Imaging Center</li>\r\n<li>Chong Zhang, CellNetworks, Univ. Heidelberg</li>\r\n<li>Nikolay Kladt, CECAD, Koeln</li>\r\n<li>Graeme Ball, Univ. Dundee</li>\r\n<li>Peter Bankhead, QUB UK, Belfast</li>\r\n<li>Olivier Burri, EPFL, Lausanne</li>\r\n<li>Torsten Stöter, LIN Magdeburg</li>\r\n<li>Laurent Gelman, FMI Basel</li>\r\n<li>Marie-Laure Boizeau, ex-Itav, Toulouse</li>\r\n<li>Nicolas Signolle,  Institut Curie, Paris</li>\r\n<li>Volker Bäcker,  MRI, Montpellier</li>\r\n</ul>\r\n\r\n#Acknowledgements\r\n\r\nThis site, biii.info, is supported by <a href="http://www.eurobioimaging.eu/content-page/wp11-data-storage-analysis">EuroBioimaging Working Package 11 "Data Storage and Analysis"</a>. The second taggathon was supported by <a href="http://france-bioimaging.org/">France BioImaging. </a>\r\n\r\n<p>Ricard Delgado Gonzalo (BIC-EPFL), together with Virginie Uhlmann and Daniel Sage (BIC - EPFL) contributed largely in setting up the tagging platform initially used during the taggathon. This prototype site could be found <a href="http://bigwww.epfl.ch/obia-tags/">here</a>.</p>\r\n\r\nDuring Oct 2013 - Nov. 2014, Biii.info existed in ImageJ.dev server. We thank Johannes Schindelin and Curtis Rueden for their kind offer and their generous supports! From Dec. 2014, the site is running in EMBL web server. We thank Agustin Villalba Casas and Matthias Helmling (IT service, EMBL Heidelberg) for their help in migrating the site. \r\n\r\n', NULL, NULL, NULL, NULL),
('2572', NULL, '11', NULL, 'Sickle cell anaemia among Eti-Turks: haematological, clinical and genetic observations.', NULL, NULL, NULL, NULL, NULL),
('2351', NULL, '139', NULL, 'Adipocyte quantification', 'Analysis of adipocyte number and size<bib>2572</bib>. The workflow URL links to the original code and example images. The full paper can be accessed [here](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3756104/). ', 'Osman S Osman, Joanne L Selway,Ma?gorzata A K?pczy?ska, Claire J Stocker, Jacqueline F O’Dowd, Michael A Cawthorne, Jonathan RS Arch, Sabah Jassim, and Kenneth Langlands', NULL, NULL, NULL),
('2573', NULL, '11', NULL, 'Active mask segmentation of fluorescence microscope images.', NULL, NULL, NULL, NULL, NULL),
('2574', NULL, '11', NULL, '{SLT-LoG: A Vesicle Segmentation Method with Automatic Scale Selection and Local Thresholding Applied to TIRF Microscopy}', NULL, NULL, NULL, NULL, NULL),
('2356', NULL, '426', NULL, 'Vesicle segmentation method', '\r\nYou can upload your image at the [Mobyle@SERPICO](http://mobyle-serpico.rennes.inria.fr/cgi-bin/portal.py#forms::ATLAS) portal and download the result. The workflow is only available online, i.e. no download possible. \r\n\r\nPublication: <bib>2574</bib>', 'A. Basset, J. Boulanger, P. Bouthemy, C. Kervrann, J. Salamero', NULL, NULL, NULL),
('2575', NULL, '11', NULL, 'Step-by-step quantitative analysis of focal adhesions', NULL, NULL, NULL, NULL, NULL),
('2576', NULL, '11', NULL, 'Spot detector based on a 3D LoG filter', 'Quote: "It applies a LoG (Laplacian of Gaussian or Mexican Hat) filter to a 2D image or to 3D volume. Here, we have a fast implementation. It is a perfect tool to enhance spots, like spherical particles, in noisy images. This module is easy to tune, only by selecting the standard deviations in X, Y and Z directions."', 'D. Sage, F.R. Neumann, F. Hediger, S.M. Gasser, M. Unser', NULL, 'http://bigwww.epfl.ch/sage/soft/LoG3D/', NULL),
('2578', NULL, '11', NULL, 'Endosomal WASH and exocyst complexes control exocytosis of MT1-MMP at invadopodia.', NULL, NULL, NULL, NULL, NULL),
('2360', NULL, '85', NULL, 'spot detection and codistribution analysis', 'WASH, Exo84, and cortactin spot detection and codistribution analysis To detect endosomes, an automatic Otsu threshold is applied to the Gaussian-filtered MT1-MMP–positive endosome image (= 1.5 pixels for the sample image). Statistics about each endosome are then saved, for example random positioning  of spots can be compared to actual positioning. \r\n\r\nFor each endosome, WASH and Exo84 (or WASH and cortactin) spots are searched for in a neighboring of x pixels (manually set by use: XX for sample data) in their respective channel. Their number and position are saved per endosome (**see the macro in Text file S2 downloadable from URL link 1**). \r\n\r\nFrom the position of WASH and Exo84 (or WASH and cortactin) spots around each endosomes, each WASH spot is paired with its closest Exo84 (or cortactin) spot neighbor, optimized over all spots around this endosome. This allowed measuring of the distribution of distance between WASH-Exo84 (or WASH-cortactin) spots (**for the co-distribution analysis, see matlab scripts in Zip file S3 downloadable from URL link 1**).\r\n\r\nURL link 2 is the publication which used this workflow <bib>2578</bib>.\r\n\r\nURL link 3 provides sample data, where parameters to be used are described above. \r\n', 'A Great Guy', NULL, NULL, NULL),
('2580', NULL, '85', NULL, 'Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells.', NULL, NULL, NULL, NULL, NULL),
('2581', NULL, '85', NULL, 'Microscope Image Correlation Spectroscopy MICS', 'Fluorescence spectroscopy by image correlation is a technique that allows analysing and characterizing the different molecular dynamics from a sequence of fluorescence images.\r\n\r\nMany image correlation techniques have been developed for different applications but in particular to study the mechanisms of cell adhesion during migration. These techniques can be used with most imaging modalities: e.g. fluorescence widefield, confocal microscopy, TIRFM. They allow to obtain information such as the density in molecules, diffusion coefficients, the presence of several populations, or the direction and speed of a movement corresponding to active transport when spatial and temporal correlations are taken into account (STICS: Spatio-Temporal Image Correlation Spectroscopy). \r\nPlease see <bib>2580</bib>  for a review and the description of the methods.\r\n\r\nThis plugin is based on ICS_tools plugin by Fitz Elliott, available [here](http://www.cellmigration.org/resource/imaging/imaging_resources.shtml "cell migration website").\r\n\r\nSome bugs have been removed, ROI does not need to be squared, fitting is weighted in order to give more weight to the smaller lags (temporal or spatial)\r\nExemple of use on sample data [fluorescent beads](http://biii.info/node/2577 "Beads")\r\n- Select an ROI, start by ICS to get the right PSF size\r\n- Then run TICS and select diffusion, or diffusion plus flow model. Remove the first line (autocorrelation) which corresponds to the noise autocorrelation before fitting.\r\n\r\n\r\n\r\n', 'Chen Chen, François Waharte,  Perrine Paul-Gilloteaux', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:microscope_image_correlation_spectroscopy:start&s[]=spectroscopy', NULL),
('2582', NULL, '85', NULL, 'ICS tools', 'Implementation of some image correlation spectroscopy tools', 'Fitz Elliott ', NULL, 'http://www.cellmigration.org/resource/imaging/imaging_resources.shtml#software', NULL),
('2476', NULL, '92', NULL, 'Fit FLIM data stored on an OMERO server', 'FLIMfit is an OMERO client-side MATLAB application for fitting and visualising time-resolved FLIM data. It supports key data formats including: Becker and Hickl .sdt, LaVision BioTech .msr, PicoQuant .bin and OME-TIFF files; as well as several options for curve fitting. Results can be saved back in OMERO, which also facilitates working collaboratively on the data analysis.', 'FLIMfit developers at Imperial College London', NULL, NULL, NULL),
('2444', NULL, '92', NULL, 'Manual Tracking with ImageJ', 'The Fiji ImageJ distribution comes with several manual tracking tools, two of which are particularly useful: \r\n\r\n* _Plugins->Tracking->Manual Tracking_\r\n* _Plugins->Tracking->Manual tracking with TrackMate_ (TrackMate is an advanced automatic tracking tool, with the option for manual editing of tracks)\r\n\r\nThe _Manual Tracking_ plugin is quick to use, intuitive and produces easy-to-understand output. TrackMate has the advantage that automatic detection and linkage can be combined with manual input.\r\n\r\nPre-processing steps before manual tracking might include:\r\n\r\n* denoising and/or deconvolution\r\n* flicker and photobleaching correction, e.g. using Fiji\'s _Image->Adjust->Bleach Correction_\r\n* flat-field correction, and/or bandpass (ImageJ\'s _Process->FFT->Bandpass filter_) according to the size of the features of interest', 'Fabrice Cordelieres; TrackMate developers', NULL, NULL, NULL),
('2484', NULL, '92', NULL, 'Particle tracking of image data stored in an OMERO database', 'u-Track is a client-side OMERO MATLAB application implementing the sophisticated multiple-particle tracking algorithm of Jaqaman et al. <bib>2479</bib>. It works on data previously imported into an OMERO server, and produces results in the form of MATLAB data structures as well as providing functionality to visualise these results.', 'Danuser lab', NULL, NULL, NULL),
('2448', NULL, '92', NULL, 'Figure creation using images in an OMERO database', 'OMERO.figure is an OMERO web application that makes generating figures from images in an OMERO image database very quick and easy. The images in the figure link back to the original data, greatly simplifying the process of adjusting the view and keeping track of original data. PDF documents are generated, which can be opened using e.g. Adobe Illustrator or Inkscape in order to produce the final finished figure.', 'Will Moore', NULL, NULL, NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2560', NULL, '85', NULL, 'Neural crest cells in mouse gut', 'Confocal microscopy stack image of the mouse midgut.  Acquisition with  ENCC enteric neural crest cells (YFP, green) and TUJ1 (red) images, with ablation of beta1 -integrin. \r\n\r\nMore information: <bib>2455<ib>.or on http://cid.curie.fr user: biii password: Biii!Tag1 search guid:10713', NULL, NULL, NULL, NULL),
('2541', NULL, '120', NULL, 'Seashell', 'The synthetic sample has been created artificially using a 3D artificial structure shape. This shape represents a seashell of six branches with various fluorophore densities. The position of the fluorophores of exactly known. Some image sequences have additional noise.\r\nThe sequences of images that we provide here simulates the PALM acquisition process: for a long sequence (2500 images, 50000 activated fluorophores) and for a short sequence (100 images, 2000 activated fluorophores).\r\nTwo version of this samples, large and flat, are articifically created. and they are available as a z-stack of images for the thick sample and as a 2D image for the flat sample. In the spatial (XY) orientation they have the shape of seashell with 6 branches of differents intensities.\r\n', NULL, NULL, NULL, NULL),
('2539', NULL, '120', NULL, 'Snow', 'In this dataset, the fluorophores are placed on a regular grid with various depth, various lateral spacing, and various number of photons\r\n\r\nhttp://bigwww.epfl.ch/smlm/datasets/index.html?p=snow', NULL, NULL, NULL, NULL),
('2577', NULL, '85', NULL, 'Fluorescent beads in solution', 'This time stack was acquired on a confocal microscope and shows free beads in water (then diffusing, but also some flow). \r\n\r\nThis is not a simulation, but some ground truth is available from the Stokes-Einstein equation allowing to compute the diffusion coefficient (free diffusion) from the beads diameter. This can be used for diffusion estimation such as temporal image correlation spectroscopy (TICS), testing spot detection and tracking algorithm, or beads diameter estimation from deconvolution or segmentation.\r\n\r\n- image acquisition frequency: 90ms\r\n- approximated PSF 0.3 Pixel\r\n-  Pixel Size 0.129 um \r\n- Beads diameter:100 nm\r\n\r\n Stokes-Einstein for spherical particles (here beads gives D= kB T/(6* pi *eta* R) where:\r\n\r\n| parameter | value |\r\n|----|----|\r\n|kB | 1,381 (Boltzmann\'s constant) 10-23 J.K-1 |\r\n| T | 293 K (absolute temperature)|\r\n| R | 100 nm (spherical particle diameter) |\r\n| eta | 0,001003 Pa.s (viscosity of water for this T |\r\n\r\nThe theoretical diffusion is then : Dth = 2.15 um2.s-1\r\n\r\nData can be accessed and downloaded in ome tiff format by logging on the url link as biii, password  Biii!Tag1, and searching for guid:10800.\r\n\r\nSome tracks results (not ground truth) are also attached to the data in the attachment panel.', NULL, NULL, NULL, NULL),
('2585', NULL, '11', NULL, 'Human Language', 'Human readable language *e.g.* English. \r\n\r\nWe are not joking, because many workflows are human-written text and called "instructions", "manuals" or tutorials. As we think that workflow could also take those forms beside scripting languages, we added human language as one of those listed in "languages" in biii. ', NULL, NULL, 'http://en.wikipedia.org/wiki/Language', NULL),
('2531', NULL, '92', NULL, 'Generate animations from image data', 'Imaris is a commercial 3D image visualisation and analysis tool. It can be used to produce complex 3D animations that include multiple volume and surface elements in several channels, as well as clipping planes and annotations such as text and arrows. Movies interpolate seamlessly between user-defined key frames, and properties such as viewing angle, zoom and visibility of each element can be changed during the animation. These features allow effective communication of results based on image data.', 'Bitplane', NULL, NULL, NULL),
('2462', NULL, '92', NULL, 'Organise Image Data using OMERO', 'OMERO is an image database application consisting of a server and several clients, the most important of which are the web client and _Insight_ java application. Metadata are extracted from images that have been imported (either using the Insight client, or directly from the filesystem), and this is accessible for search. A standardised hierarchy of _Project > Dataset > Image_ in which image thumbnails can be viewed, combined with group membership, tagging, and attachment of results and other files gives a powerful framework for organising scientific image data. Images can also be analysed server-side or client-side within the base OMERO application or one of its many extensions.\r\n\r\nOMERO has APIs for extension in multiple languages: java, python, C++ and MATLAB; and such extensions have easy access to the image data and metadata in the database.', 'OME Developers', NULL, NULL, NULL),
('1818', NULL, '0', NULL, 'Cell Counter', 'Assists the manual counting of cells. ', 'Kurt De Vos', NULL, 'http://imagej.net/plugins/cell-counter.html', NULL),
('2348', NULL, '92', NULL, 'Count Cells Manually', 'An ImageJ plugin (author: Kurt De Vos) for counting multiple cell classes manually, which overlays already-counted cells on the image. It is controlled via its own graphical user interface, and can export and load results.', 'Kurt De Vos', NULL, NULL, NULL),
('2586', NULL, '11', NULL, 'High-throughput, subpixel precision analysis of bacterial morphogenesis and intracellular spatio-temporal dynamics.', NULL, NULL, NULL, NULL, NULL),
('2412', NULL, '92', NULL, 'MicrobeTracker', 'MicrobeTracker is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes<bib>2586</bib>. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. [Lesterlin et al. (2014)](http://www.ncbi.nlm.nih.gov/pubmed/24362571).', 'Graeme Ball', NULL, NULL, NULL),
('2587', NULL, '11', NULL, 'MicrobeTracker', 'Quote: *MicrobeTracker is a MATLAB-based program designed to detect and outline bacterial cells in images obtained by light microscopy and to analyze fluorescence signal inside them.*\r\n\r\n', 'Sliusarenko O, Heinritz J, Emonet T, and Jacobs-Wagner C', NULL, 'http://microbetracker.org/help/helpMicrobeTracker.htm', NULL),
('2588', NULL, '11', NULL, 'SpotFinderZ', 'Quote: *SpotFinderZ (from now on simply SpotFinder) detects round, usually diffraction-limited spots inside bacterial cells outlined with MicrobeTracker and places them into the meshes structure produced by MicrobeTracker. The program is written in MATLAB and saves the data in the MicrobeTracker format by appending additional fields.*', NULL, NULL, 'http://microbetracker.org/help/helpSpotFinder.htm', NULL),
('2589', NULL, '11', NULL, 'SpotFinderM', 'Quote: *A GUI-based program which manually detects spots and places them into previously detected meshes. Currently the program runs from MATLAB only. *', 'Sliusarenko O, Heinritz J, Emonet T, and Jacobs-Wagner C', NULL, 'http://microbetracker.org/help/helpSpotFinderM.htm', NULL),
('2143', NULL, '0', NULL, 'ImageJ/QuimP', 'Semi-automatic tracking and quantification of fluorescence intensity at cell boundary of single migrating cells. 4 functions are packaged in one:\r\n\r\n- BOA: Segmentation and tracking of cell migration based on active contour (snake)\r\n- ECMM: Dynamic mapping of moving contour. \r\n- ANA: Measurement of fluorescence dynamics along cell membrane\r\n- Q: Time-space plots (kymograph) of \r\n  - convexity\r\n  -  fluorescence\r\n  - membrane speed\r\n  - and more...\r\n', NULL, NULL, 'http://www2.warwick.ac.uk/fac/sci/systemsbiology/staff/bretschneider/quimp/', NULL),
('2590', NULL, '11', NULL, 'High Resolution Tracking of Cell Membrane Dynamics in Moving Cells: an Electrifying Approach', NULL, NULL, NULL, NULL, NULL),
('2566', NULL, '137', NULL, '3D dataset of astrocytes in mouse cortex, two time frames', 'CPP labeled astrocytes in mouse cortex.\r\n\r\nThe astrozytes were imaged two times with a delay of 27 minutes. The animal was removed from the microscope during the waiting phase. Thus, there is some shift between the two frames.\r\n\r\nVoxel size:\r\nx: 500 nm\r\ny: 500 nm\r\nz: 2000 nm\r\n', NULL, NULL, NULL, NULL),
('2591', NULL, '426', NULL, 'FindFoci: A Focus Detection Algorithm with Automated Parameter Training That Closely Matches Human Assignments, Reduces Human Inconsistencies and Increases Speed of Analysis.', NULL, NULL, NULL, NULL, NULL),
('2593', NULL, '426', NULL, 'FindFoci', 'The FindFoci plugins allow the identification of peak intensity regions within 2D and 3D images. The algorithm is highly configurable and parameters can be optimised using reference images and then applied to multiple images using the batch mode.\r\nDetails of the benefits of training an algorithm on multiple images can be found in the FindFoci paper: \r\n<bib>2591</bib>', 'Alex Herbert', NULL, 'http://www.sussex.ac.uk/gdsc/intranet/microscopy/imagej/findfoci', NULL),
('2386', NULL, '85', NULL, 'Microtubule Length Analysis', '###Task\r\n\r\nQuantify the length of microtubules (MT) and the MT average density per cell. \r\n\r\n### Workflow descriptions\r\n\r\nSimple two step workflow, allowing visual & manual correction of microtubule between the 2 steps. Batch measurement of microtubule lengths for multiple images is achieved by segmenting the MTs and then their skeletonizations.  The number of pixels in the microtubule is proportional to their length, so the length can be estimated.\r\n\r\n### Script\r\n\r\nWorkflow is written as an ImageJ macro (Fiji) with following steps:\r\n\r\n1. The enhancement of tubular structure by computing eigenvalues of the hessian matrix on a Gaussian filtered version of the image ( sigma 1 pixel), as implemented in the tubeness plugin. \r\n2. The tubules were then thresholded , and structures containing less than 3 pixels were discarded. \r\n3. If needed, a visual check and correction of segmented microtubule is then performed.  \r\n4. After correction, segmented MTs were then reduced to a 1-pixel thick line using the skeletonize plugin of Fiji.  The length of the skeletonized microtubules was then directly proportional to their length. \r\n5. Data were grouped by condition and converted back to micrometers units under Matlab for the statistical tests.\r\n\r\n#### Pitfalls\r\n\r\nCommented but not that general without editing some fields in the macros.\r\n\r\n### Sample Data\r\n\r\nSample data and workflow (see above URL) can be accessed by\r\n\r\n-  login: biii\r\n-  password Biii!Tag1\r\n\r\n## Misc\r\n\r\n3D version also available here.\r\n\r\nUse of components Skeletonize and Tubeness Filter\r\n\r\n', 'Perrine Paul-Gilloteaux', NULL, NULL, NULL),
('2435', NULL, '137', NULL, '2D cell tracking and analysis of morphological dynamics', 'The QuimP software from Bretschneider group is depolyed as imageJ plugin and includes model-based cell segmentation, cell outline tracking and quantification of spatially resolved speed of protrusions and retractions<bib>2590</bib>.\r\n\r\nThe algorithm to calculate morphological dynamics is very fast compared to <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1367294/">other approaches</a> \r\n', 'R.A. Tyson, D.B.A. Epstein, K.I. Anderson and T. Bretschneider', NULL, NULL, NULL),
('2395', NULL, '92', NULL, 'Imaris Tracking', 'ImarisTrack allows 3D tracking of spots and objects, with straightforward manual adjustment of automatic tracking results.', 'Bitplane', NULL, NULL, NULL),
('2063', NULL, '11', NULL, 'Bleach Correction', 'Three different methods for correcting fluorescence bleaching. \r\n\r\n1. Simple (framewise ratio based)\r\n2. Exponential (curve fitting with exponential decay model)\r\n3. Histogram matching (register histogram shape. with 16 bit, it takes long time... it should be improved).\r\n\r\n\r\n', 'Kota Miura, Jens Rietdorf', NULL, 'http://fiji.sc/Bleach_Correction', NULL),
('2353', NULL, '93', NULL, 'Analyzing gels and western blots with ImageJ', 'It explains how to use ImageJ to compare the density (aka intensity) of bands on an agar gel or western blot. \r\nSome notes can be found here: http://cellnetmcweb.bioquant.uni-heidelberg.de/image-analysis/ShortTutorials/Fiji_GelAnalyzer.pdf', 'Luke Miller (contact@lukemiller.org)', NULL, NULL, NULL),
('2363', NULL, '139', NULL, 'Quantitative analysis of focal adhesions', 'Simple workflow description for ImageJ, step-by-step description. No analysis of focal adhesion dynamics<bib>2575</b>.', 'Utku Horzum, Berrin Ozdil, Devrim Pesen-Okvur', NULL, NULL, NULL),
('2526', NULL, '120', NULL, 'Localization microscopy filtering using PALMsiever', 'Sieving (or filtering) is choosing the good localizations and discarding the false ones. This operation is performed by inspecting the distribution of the localizations\' fitted parameters and changing the min and max accordingly.', 'Thomas Pengo, Seamus Holden', NULL, NULL, NULL),
('2565', NULL, '93', NULL, 'Large data storage in hdf5 using Fiji/ImageJ', '<a href="http://hdf.ncsa.uiuc.edu/HDF5/">HDF5</a> is a data format for storing extremely large and complex data collections. This Fiji/ImageJ HDF5 plugin saves and loads 2D - 5D datasets with flexible options.', 'Olaf Ronneberger', NULL, NULL, NULL),
('2595', NULL, '11', NULL, 'HDF5 Plugin for ImageJ and Fiji', NULL, NULL, NULL, 'http://lmb.informatik.uni-freiburg.de/resources/opensource/imagej_plugins/hdf5.html', NULL),
('2507', NULL, '120', NULL, 'RapidSTORM usage tutorial', 'This tutorial will exemplify basic rapidSTORM usage by showing how to convert an Andor SIF acquisition to a super-resoluted image with rapidSTORM.', 'RapidSTORM authors', NULL, NULL, NULL),
('2510', NULL, '444', NULL, 'Trainable Weka Segmentation ImageJ Macro', 'Macro using the plugin Trainable Weka Segmentation on leaf image of imageJ\r\n\r\nThis plugin can be trained to learn from the user input and perform later the same task in unknown (test) data.\r\nWeka: it makes use of all the powerful tools and classifiers from the latest version of Weka.\r\nSegmentation: it provides a labeled result based on the training of a chosen classifier. Trainable Weka Segmentation\r\n\r\nComplete macro example is at the end of the page. \r\n\r\n', 'Ignacio Arganda-Carreras, Albert Cardona, Verena Kaynig, Johannes Schindelin', NULL, NULL, NULL),
('1612', NULL, '0', NULL, 'Feature Extraction', 'Automatic finding of image features are very convenient for registering two images to align them in proper orientation. \r\nTwo image plugins are implemented for extracting image features and are placed as menu items at:\r\n\r\n[Plugins > Feature Extraction > Extract SIFT Correspondences]\r\n\r\nand \r\n\r\n[Plugins > Feature Extraction > Extract MOPS Correspondences]\r\n\r\nFor more details, see the linked page in Fiji wiki. For details about SIFT algorithm, see <bib>2569</bib>. For more details about MOPS algorithm, see <bib>2570</bib>. \r\n\r\n\r\n\r\n\r\n\r\n\r\n', 'Stephan Saalfeld', NULL, 'http://fiji.sc/Feature_Extraction', NULL),
('2579', NULL, '85', NULL, '2D and 3D tracking based on global cost function optimization', 'The workflow consists of firstly identifying spot (which can be also gravity center of cells identified by another method), and then secondly compute trajectories by linking these spots by global optimisation with a cost function. This method is part of the methods evaluated in <bib>2548</bib> and is described in detail in its supplementary section. \r\n\r\n### Dependencies\r\n\r\nFollowing plugins are required.  \r\n\r\n1. [JAR to be placed under IJ plugin directory](http://xfer.curie.fr/get/192esfJKxA1/Trackingv2.0.zip)\r\n  1. A pdf file with instructions and output description is also available in the zip .\r\n1. [MTrackJ]() : Used for visualization of tracks. Preinstalled in Fiji.\r\n2. [Imagescience.jar](http://www.imagescience.org/meijering/software/download/imagescience.jar):  This library is used by MTrackJ. Preinstalled in Fiji.\r\n3. [jama.jar](http://math.nist.gov/javanumerics/jama/). Preinstalled in Fiji.\r\n\r\n###Advantages: \r\n\r\n-  support blinking (with a parameters allowing it or not)\r\n-  fast, \r\n-  can be used in batch, some analysis results provided.\r\n-  No dynamic model.\r\n-  The tracking part is not dependant of ImageJ.\r\n\r\n### Pitfalls: \r\n-  does not support division, \r\n-  the optimization algorithm used is a simulated annealing, so results can be slightly different between two runs. \r\n-  No Dynamic model (so less good results but can be used for a first study of the kind of movements)\r\n\r\n##The sample data \r\n\r\nThe parameters used  for this example data Beads, were \r\n\r\n1.  detection: 150 \r\n2. the max distance in pixels: 20 \r\n3. max allowed disappearance in frame: 1\r\n', 'Perrine Paul-Gilloteaux', NULL, NULL, NULL),
('2532', NULL, '120', NULL, 'Tracing data using PALMsiever', 'Tracer allows the user to create a trace along a structure in an image. It uses the underlying molecule positions, not the rendered image.', 'Thomas Pengo', NULL, NULL, NULL),
('2312', NULL, '11', NULL, 'Microtubules Tool (3D)', 'The tool measures the total length of the microtubules in a 3D image.\r\n', 'Volker Baecker', NULL, NULL, NULL),
('2571', NULL, '11', NULL, 'HowTo', '## Usage\r\n\r\n**biii.info is for searching bioimage analysis tools** (functions, plugins, toolbox, scripts, macros, instructions). Please use the search box in the sidebar. You could narrow your search to components, workflows, sample images, software packages or languages. Your search term could be any keywords that you are interested in. \r\n\r\nAt the moment, biological terms might not have many hits, as we still need to tag pages, and your contribution by tagging will be a big help! \r\n\r\n## Terms\r\n\r\nWe use following terms for categorizing bioimage analysis tools. \r\n \r\n- **Components** \r\n  - Implementation of an image processing / analysis algorithm\r\n  - e.g. A single menu item in image processing software\r\n  - e.g. A plugin / module / add-ons. For plugins that bundles multiple tools, each tool will be the component and the plugin as a whole cannot be a component. Please place the plugin as "Software / Library".\r\n  - e.g. A class in image processing library. \r\n- **Workflow**\r\n  - A series of image processing using several ***components* to start with image data as input and outputs either processed image or numerical values.\r\n  - e.g. a script\r\n  - e.g. a detailed step-by-step instruction. \r\n- **Software / Library**\r\n  - A package of components. \r\n- **Sample Data**\r\n  - In general, sample image data. It could be none-image if needed. \r\n  - Uploaded image is strongly recommended to have details of the object in image, capture conditions e.g. microscope, objective, probe details, scale and so on.\r\n  - Better be with explicit licensing condition. \r\n\r\n- Details on our current nomenclature is explained in [here](https://docs.google.com/document/d/1UZ_M8RIX8TZRh6sXA_h3lSO4cELey66Bwe6ISONMRlo/edit) \r\n\r\n## Get involved\r\n\r\nWe welcome you to add and/or edit pages. To do so please first get your user account.\r\n\r\n- [Registration page](http://biii.info/user/register)\r\n\r\n## Adding / Editing pages\r\n\r\nInstruction for adding component and workflow pages are [here](http://biii.info/node/2346)\r\n\r\n## Roadmap\r\n\r\nbiii.info is still under development, and our roadmap is schemed in the illustration below. \r\n\r\n<img src="/files/biii/BiiiInformationFlow_0.png" width="400" height="300" alt=""  />\r\n\r\nTo read more about our roadmap, please visit:  [RoadMap](http://biii.info/node/2282).', NULL, NULL, NULL, NULL),
('2584', NULL, '92', NULL, 'ImageJ Bandpass filter', 'The Fourier transform of an image produces a representation in frequency space: i.e. separated according to spatial frequency (effectively scale). The 2D amplitude map of the different spatial frequencies is symmetrical, and is commonly displayed with low spatial frequencies (large features) in the centre, highest spatial frequencies (small features) at the edges.\r\n\r\nFourier filtering involves suppressing or enhancing features in the Fourier domain before carrying out an inverse Fourier transform to obtain a filtered real-space image. ImageJ\'s _Process > FFT > Bandpass Filter_ implements two common Fourier-filtering functions: 1. filtering for specific sizes of feature in an image by selecting minimum and maximum feature sizes (selecting a radial band of frequencies in Fourier space); 2. filtering out repetitive horizontal or vertical stripes by cutting out a zero-frequency stripe in the orthogonal direction in frequency space.\r\n\r\nThe example image above shows the effect of filtering for 2 feature size ranges: 0-8 pixels, and 8-256 pixels; where the former appears "flattened" or washed-out, and the latter very blurred. The small images displayed to the lower-right of each filtered image correspond to the mask applied to the Fourier transform. Such filtering can be useful prior to global thresholding, for noise suppression, etc.', 'Joachim Walter', NULL, 'http://rsb.info.nih.gov/ij/plugins/fft-filter.html', NULL),
('1843', NULL, '0', NULL, 'Watershed Algorithm', NULL, NULL, NULL, 'http://fiji.sc/Watershed_Algorithm_(ImageJ_plugin)', NULL),
('2310', NULL, '11', NULL, 'Cell segmentation by EBImage, R', NULL, 'takeo katsuki', NULL, NULL, NULL),
('2338', NULL, '11', NULL, 'Centromere Recognition and Quantification', 'Particle detection is based on "Analyze Particles" in ImageJ. It probably could also be used in spot detection, not limited to centromere. \r\n\r\n>This macro is described in Bodor et al. (2012) Analysis of Protein Turnover by Quantitative SNAP-Based Pulse-Chase Imaging. Current Protocols in Cell Biology, UNIT 8.8. The macro recognizes centromere or kinetochore foci in Delta Vision or TIFF images and determines their centroid position. Fluorescent intensities are then measured for each centromere by placing a small box around the centroid position of the centromere. The peak intensity value within the box is corrected for local background by subtraction of the minimum pixel value. This process results in an accurate measurement of large numbers of centromere or kinetochore-specific signals.', 'A Great Guy', NULL, NULL, NULL),
('2373', NULL, '92', NULL, '3D Object Segmentation and Tracking', 'In the commercial image analysis software "Volocity", automated Measurement protocols can be constructed by dragging, dropping and configuring a sequence of individual "tasks". By combining the "Find Objects" task with a subsequent "Track" task, 3D objects can be identified and followed over time. The initial "Find Objects" segmentation can be refined, e.g. using "Separate Touching Objects"; and tracking results in the form of "Measurement Items" can be viewed in tabular form, as a graph, etc.', 'A Great Guy', NULL, NULL, NULL),
('2597', NULL, '11', NULL, 'Curvelet analysis of kymograph for tracking bi-directional particles in fluorescence microscopy images', NULL, NULL, NULL, NULL, NULL),
('2371', NULL, '139', NULL, 'Kymograph analysis for particle tracking', 'Generation of Kymographs using 2D+t images. In the generated kymographs, objects can be tracked and the results are visualized <bib>2597</bib>.\r\n\r\n', 'Chenouard, N., Buisson, J., Bloch, I., Bastin, P. and Olivo-Marin, J.-C.', NULL, NULL, NULL),
('2598', NULL, '11', NULL, 'A high-level 3D visualization API for Java and ImageJ.', NULL, NULL, NULL, NULL, NULL),
('1749', NULL, '0', NULL, '3D Viewer', 'Paper to be cited for this plugin: <bib>2598</bib>', 'Benjamin Schmid', NULL, 'http://fiji.sc/3D_Viewer', NULL),
('2599', NULL, '11', NULL, 'TransformJ', 'A Java Package for Geometrical Image Transformation, works up to 5D. \r\n\r\n- Affine\r\n- Crop\r\n- Embed\r\n- Matrix\r\n- Mirror\r\n- Rotate\r\n- Scale\r\n- Translate\r\n- Turn\r\n', 'Erik Meijering', NULL, 'http://www.imagescience.org/meijering/software/transformj/', NULL),
('1989', NULL, '0', NULL, 'KymographTracker', NULL, NULL, NULL, 'http://icy.bioimageanalysis.org/plugin/KymographTracker', NULL),
('2367', NULL, '75', NULL, 'Speckle Counting', 'This pipeline shows how to identify smaller objects (foci) within larger objects (nuclei) and how to use the Relate module to establish a relationship between the two as well as perform per-object aggregate measurements (such as number of foci per nucleus).', 'Cellprofiler team', NULL, NULL, NULL),
('1699', NULL, '0', NULL, 'RelateObjects', NULL, NULL, NULL, 'http://www.cellprofiler.org/CPmanual/RelateObjects.html', NULL),
('2365', NULL, '75', NULL, 'Colocalisation in 2-D', 'Example pipeline from http://www.cellprofiler.org/examples.shtml\r\n\r\nMeasuring the colocalization between fluorescently labeled molecules is a widely used appraoch to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identifcation and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels.', 'Cellprofiler Developers', NULL, NULL, NULL),
('1610', NULL, '0', NULL, 'Rolling Ball Background Subtraction', NULL, NULL, NULL, 'http://fiji.sc/Rolling_Ball_Background_Subtraction', NULL),
('2601', NULL, '11', NULL, 'Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.', NULL, NULL, NULL, NULL, NULL),
('2603', NULL, '11', NULL, 'Adiposoft', 'Quote "Adiposoft is an automated Open Source software for the analysis of adipose tissue cellularity in histological sections."\r\n', 'Miguel Galarraga, Javier Campión, Arrate Muñoz-Barrutia, Noemí Boqué, Haritz Moreno, José Alfredo Martínez, Fermín Milagro, and Carlos Ortiz-de-Solórzano', NULL, 'http://fiji.sc/Adiposoft', NULL),
('2355', NULL, '139', NULL, 'Adipocyte quantification', 'Example data can be found on the plugin description page. There is also a link to a Matlab version of the workflow.', 'Miguel Galarraga, Javier Campión, Arrate Muñoz-Barrutia, Noemí Boqué, Haritz Moreno, José Alfredo Martínez, Fermín Milagro, and Carlos Ortiz-de-Solórzano1', NULL, NULL, NULL),
('2604', NULL, '11', NULL, 'Automated segmentation and tracking for large-scale analysis of focal adhesion dynamics.', NULL, NULL, NULL, NULL, NULL),
('2378', NULL, '137', NULL, 'Tracking of focal adhesions in 2D time lapse movies', 'Tracking of focal adhesions includes a number of challenges:\r\n\r\n1. Detection of focal adhesion regions in areas of highly variable backgroud\r\n2. Sepearation of "clumped" adhesions in different objects.\r\n3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.\r\n\r\nThe autors describe how to detect focal adhesion objects and how to track them over time<bib>2604</bib>. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions. \r\n\r\nThe authors implemented the workflow in Matlab, but do not provide a ready to use script.\r\n', 'T. WÜRFLINGER,     I. GAMPER,     T. AACH and     A.S. SECHI', NULL, NULL, NULL),
('1450', NULL, '0', NULL, 'Trainable Weka Segmentation', NULL, 'Ignacio Arganda-Carreras, Albert Cardona, Verena Kaynig, Johannes Schindelin', NULL, 'http://fiji.sc/Trainable_Weka_Segmentation', NULL),
('2605', '2605', '426', NULL, 'ISE-MeshTools', 'ISE-MeshTools is a software designed by Renaud Lebrun, from the university of Montpellier II. ISE-MeshTools is a\r\nsystem for the processing and editing of series of 3D triangular meshes. The system provides a set of tools for editing,\r\npositioning, deforming, labelling, measuring and rendering sets of 3D meshes.\r\nFeatures include:\r\n• Retrodeformation for un-deforming fossils/deformed specimens\r\n• Point and curve primitives for placing the exact type of landmark points you’re interested in\r\n• Easy to use 3D interface for positioning and manipulating sets of surfaces and landmark primitives\r\n• Mesh tagging, labelling and colouring (to allow for the creation of anatomy atlases)\r\n• Mesh scalar computation and colouring (based upon curvature/thickness etc...) \r\n\r\n Lebrun, R., ISE-MeshTools, a 3D interactive fossil reconstruction freeware., 12th Annual Meeting of EAVP, Torino, Italy ; 06/2014 ', 'Renaud Lebrun', NULL, 'http://morphomuseum.com/tutorialsMeshtools', NULL),
('2606', NULL, '11', NULL, 'KNIME Workflow', 'KNIME allows you to interactively compose workflow using GUI. The workflow you created can be saved as a zip file and imported later to redo the analysis. \r\n\r\nInside the workflow zip file is multiple folders called "Nodes", each containing parameter settings for components (functions, or image processing algorithm implementations). In a sense, KNIME workflow is a structured set of XML files that specifies the processing parameters and the order of processing. ', NULL, NULL, 'http://tech.knime.org/getting-started', NULL),
('2491', NULL, '93', NULL, '3D object based colocalization using KNIME', 'These two similar KNIME workflow solutions take 3D data stacks to segment the spots first, using local thresholding with subsequent morphological operations in order to remove noise. Colocalization is then defined by overlapping or center point distance between segmented objects. Further filtering such as overlapping ratio or distance range is done through KNIME table processing. ', 'Juergen Reymann', NULL, NULL, NULL),
('2474', NULL, '93', NULL, '2D spots counting using KNIME', 'These two KNIME workflow solutions are similar: first one detects nuclei and spots inside the nuclei without taking care of surrounding regions, i.e. mitochondria. The second one provides the full\r\nsolution including spots in mitochondria.', 'Juergen Reymann', NULL, NULL, NULL),
('2481', NULL, '93', NULL, '2D tracking using KNIME', 'This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.', 'Juergen Reymann', NULL, NULL, NULL),
('2607', NULL, '11', NULL, 'idTracker: tracking individuals in a group by automatic identification of unmarked animals.', NULL, NULL, NULL, NULL, NULL),
('1472', NULL, '0', NULL, 'Auto Local Threshold', NULL, NULL, NULL, 'http://fiji.sc/Auto_Local_Threshold', NULL),
('2594', NULL, '11', NULL, 'FeatureJ Laplacian', 'An often used Laplacian filter for enhancing signals at object boundaries and dots. It works with XY, XYZ, XYZ-T, XYZ-T-Ch1, XYZT-C1-C2 images. \r\n\r\nDistributed as a part of ImageJ plugin [FeatureJ](http://imagescience.org/meijering/software/featurej/), and included in Fiji.  \r\nThe second URL above is the link to its Javadoc. (imagescience.feature.Laplacian). \r\n\r\nA primer for using this class in Jython script is in [CMCI Jython/Fiji cookbook: FeatureJ](http://cmci.embl.de/documents/120206pyip_cooking/python_imagej_cookbook#laplacian)', 'Erik Meijering', NULL, 'http://imagescience.org/meijering/software/featurej/laplacian/', NULL),
('1959', NULL, '0', NULL, 'Enhance Local Contrast (CLAHE)', NULL, NULL, NULL, 'http://fiji.sc/Enhance_Local_Contrast_(CLAHE)', NULL),
('2600', NULL, '11', NULL, 'Find Maxima', NULL, 'Michael Schmid', NULL, 'http://rsbweb.nih.gov/ij/docs/guide/146-29.html#toc-Subsection-29.4', NULL),
('2517', NULL, '120', NULL, 'Evaluate spectral demixing measurement', '(from the webpage)\r\n\r\n>This usage example shows how to produce two-color images from spectrally unmixed data sets. It was written for an Alexa647/Alexa700 measurement on the Würzburg 1 biplane setup as documented in [Aufmkolk2012]. The first two tasks in this example produce prerequisite knowledge for the image generation, the alignment information (Produce linear alignment matrix) and the F2 ratios, i.e. the relative intensity of fluorophores between the channels.\r\n\r\n[Aufmkolk2012] Hochauflösende Mehrfarben-Fluoeszenzmikroskopie. Sarah Aufmkolk. Julius-Maximilians-Universität Würzburg. 2012-mar.', 'RapidSTORM authors', NULL, NULL, NULL),
('2473', NULL, '139', NULL, 'Performing a Fluorescence Recovery after Photobleaching (FRAP) experiment', 'The article describes how a FRAP experiment can be conducted and subsequently analyzed. This includes steps in ImageJ and subsequent normalization of the intensity data.', 'Chan-Ying Zheng, Ronald S. Petralia, Ya-Xian Wang, Bechara Kachar', NULL, NULL, NULL),
('2340', NULL, '11', NULL, 'Histo_surfaces', ' * ImageJ macro for calculating empty surfaces on histological slices (ex: tubules in a kidney).', '[Fabrice Duprat](https://www.ipmc.cnrs.fr/~duprat/ipmcgb.htm#debut)', NULL, NULL, NULL),
('2341', NULL, '11', NULL, 'Counting Clumped Cells', ' * A workflow combining ImageJ macro and manually using Trainable Weka Segmentation plugin for counting clumped cells. ', '[kaye11](http://stackoverflow.com/users/2307398/kaye11), Curtis Ruden', NULL, NULL, NULL),
('2305', NULL, '11', NULL, 'Golgi Segmentation by Active Mask framework', 'ImageJ plugin and Matlab m-file. Paper: <bib>2573</bib>', 'G. Srinivasa, M. C. Fickus, Y. Guo, A. D. Linstedt and J. Kova?evi?', NULL, NULL, NULL),
('2311', NULL, '11', NULL, 'Arabidopsis Seedlings Tool', 'The Arabidopsis Seedlings Tool allows to measure the surface of green pixels per well in images containing multiple wells. It can be run in batch mode on a series of images. It writes a spreadsheet file with the measured area per well and saves a control image showing the green surface that has been detected per well.\r\n\r\nThe workflow is used in <bib>2468</bib>. It is described in <bib>2471</bib>', 'Volker Baecker', NULL, NULL, NULL),
('1607', NULL, '0', NULL, 'Bio-Formats', 'Bio-Formats is a standalone Java library for reading and writing life sciences image file formats. It is capable of parsing both pixels and metadata for a large number of formats, as well as writing to several formats.\r\n\r\nThe primary goal of Bio-Formats is to facilitate the exchange of microscopy data between different software packages and organizations. It achieves this by converting proprietary microscopy data into an open standard called the <a href="http://www.openmicroscopy.org/site/support/ome-model/">OME Data Model</a>, particularly into the <a href="http://www.openmicroscopy.org/site/support/ome-model/ome-tiff/index.html">OME-TIFF</a> file format.\r\n\r\n### Command Line Tools\r\n\r\nBioformats could also be used as stand alone application from command line. See [Bioformats command line tools introduction.](http://www.openmicroscopy.org/site/support/bio-formats5/users/comlinetools/) \r\n\r\n', 'The OME Consortium, http://www.openmicroscopy.org/site/about/who-ome', NULL, 'http://www.openmicroscopy.org/site/products/bio-formats', NULL),
('2357', NULL, '469', NULL, 'Wound healing assay: analysis', '<blockquote>This macro was designed to measure the size of the scratch wound in a wound scratch assay. It uses an edge-detection and thresholding technique. It will batch process all images in a directory. Images captured by time-lapse should be compiled into stacks using a tool similar to "Metamorph nd & ROI files importer (nd stack builder)" by Fabrice P. Cordelières. Images to be analyzed should be placed in one directory (Source Directory). A second directory should be created to save results files and images (Destination Directory). Setting correct Lower and Upper thresholds is important to obtain a good result.</blockquote>\r\n\r\n<em>Two macros are available, one using edge detection, the second one using background subtraction.</em>', 'Stuart J Gallagher; William J Ashby; Fabrice P Cordelières; Lionel Larue', NULL, NULL, NULL),
('2416', NULL, '469', NULL, 'Wound healing assay: analysis', '<blockquote>In this example, cells are grown as a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the area occupied by the tissue sample.</blockquote>', 'A Great Guy', NULL, NULL, NULL),
('2596', NULL, '11', NULL, 'MTrackJ', 'Manual Tracking GUI. Many shortcut keys, and after being experienced, manual tracking can efficiently done. Post-editing capability to delete segments, merge and splitting tracks is quite useful.  \r\n', 'Erik Meijering', NULL, 'http://www.imagescience.org/meijering/software/mtrackj/', NULL),
('2393', NULL, '469', NULL, 'Find cells using nuclear and membrane fluorescence', '<blockquote>This protocol first extracts the cell nuclei from a given fluorescence channel (full labeling), and grows a contour from each nucleus to extract the cell edge in another fluorescence channel (membrane-labeling).</blockquote>', 'Alexandre Dufour', NULL, NULL, NULL),
('2608', '2608', '11', NULL, 'Pandore', '<blockquote>\r\nPandore is a standardized library of image processing operators. The current version contains image processing operators that operate on grayscale, color and multispectral, 1D, 2D and 3D images. \r\n</blockquote>\r\n\r\nOperator Index: <https://clouard.users.greyc.fr/Pandore/programmes/en/index_operatorsP0.html>', 'Clouard et. al.', NULL, 'https://clouard.users.greyc.fr/Pandore/', NULL),
('2610', NULL, '11', NULL, 'Whole slide quantification of stromal lymphatic vessel distribution and peritumoral lymphatic vessel density in early invasive cervical cancer: a method description.', NULL, NULL, NULL, NULL, NULL),
('2369', NULL, '94', NULL, 'FISH signals detection', 'The macro segments and classifies human spermatozoids nuclei (DAPI) based on the number of FISH signals (spots) they contain. It reports the percentage of occurrences of user defined classes (combinations of spot multiplicity in the FISH channels) as well as the position (point selections) of the detected nuclei falling in these classes. The input image should be an hyperstack with 4 channels: DAPI (first channel) and three FISH channels. The images are typically obtained as a maximum intensity projection of few channels (confocal) or a single z slice acquisition (widefield).', 'Sébastien Tosi', NULL, NULL, NULL),
('1945', NULL, '0', NULL, 'mcib3d library', 'Plugins for 3D Image processing and Analyisis in ImageJ.\r\nPreviously (?) known as [3D ImageJ Suite](http://imagejdocu.tudor.lu/doku.php?id=plugin:stacks:3d_ij_suite:start).', 'Thomas Boudier', NULL, 'http://icy.bioimageanalysis.org/plugin/mcib3d library', NULL),
('2611', NULL, '11', NULL, 'TANGO Tools for Analysis of Nuclear Genome Organization', '## About\r\n<blockquote>\r\nTANGO software is an open-source software for Analysis of Nuclear Genome Organization. It is composed of an ImageJ plugin for batch processing and analysis, and a R package for statistical analysis.\r\n</blockquote>\r\n\r\nReference: <bib>2528</bib>\r\n\r\n## Some key features\r\n- Image import uses bioimage formats.\r\n- Construction of workflow in GUI by choosing filters / segmentation strategy for\r\n   - Prefiltering\r\n   - Segmentation\r\n   - Postfiltering\r\n- Isolated nuclei could individually be inspected, deleted from list and subjected for detailed analysis. \r\n- Uses [MCIB3D library](http://biii.info/?q=node/1945) as backend. \r\n- Basic usage is to segment nucleus, crop them to single nucleus objects, segment substructures within objects and measure their properties. \r\n- Optionally R can be connected to do detailed analysis of results. \r\n- Uses MongoDB to manage huge data set. \r\n\r\n', 'Jean Ollion, Julien Cochennec, Christophe Escudé, François Loll, Thomas Boudier ', NULL, 'http://biophysique.mnhn.fr/tango/HomePage', NULL),
('1851', NULL, '0', NULL, 'SpotDistance', '<blockquote>\r\nAn ImageJ plugin for evaluate intra-nuclear 3D cross-distances between fluorescent spots in multi-channel images\r\n</blockquote>\r\n\r\n![image](http://bigwww.epfl.ch/sage/soft/spotdistance/meta/splash.png)\r\n', 'Daniel Sage', NULL, 'http://bigwww.epfl.ch/sage/soft/spotdistance/', NULL),
('2612', NULL, '11', NULL, 'Non-Local Means Denoising', NULL, NULL, NULL, NULL, NULL),
('2613', NULL, '11', NULL, 'Auxin depletion from leaf primordia contributes to organ patterning.', NULL, NULL, NULL, NULL, NULL),
('2615', NULL, '11', NULL, 'An unbiased detector of curvilinear structures', NULL, NULL, NULL, NULL, NULL),
('2617', NULL, '11', NULL, 'NIH Image to ImageJ: 25 years of image analysis', NULL, NULL, NULL, NULL, NULL),
('2294', '2294', '2', NULL, 'ImageJ', 'BioImage Analysis Tool for all! See <bib>2617</bib>.\r\n\r\nImageJ 1.x <http://imagej.net/index.html>\r\n\r\nImageJ2 <http://imagej.net> ', 'Wayne Rasband', NULL, 'http://imagej.net', NULL),
('2618', NULL, '11', NULL, 'added a patch for PDO Exception', '20150220\r\n\r\npatched a fix recommended in: <https://www.drupal.org/node/1906626>\r\n\r\n<https://www.drupal.org/files/invalid_utf8_database_errors-1757488-1.patch>', NULL, NULL, NULL, NULL),
('2619', NULL, '11', NULL, 'Recent crash and recovery', 'During the period between Feb 12 -15th, there was a serious clash that caused biii to become white out: nothing shown. \r\n\r\nThis probably was due to complex of reasons: one obvious problem before it became so damaged, was that authentification settings for each pages were collapted, and white out happened after trying to fix this from the web interface. Updating the "allowed" actions in "node access" setting exhausted memory. \r\n\r\nBut then by adding some lines in index.php to find out what is happening, memory exhaustion was occurring in biblio module. \r\n\r\nTuring off biblio module from drush using command\r\n\r\n    drush dis biblio\r\n\r\nRegain the page to appear in the web, but without biblio. When turned on, website again became whiteout. Memory always run out, with a warning \r\n\r\n<blockquote>\r\nFatal error: Allowed memory size of 536870912 bytes exhausted (tried to allocate 512 bytes) in /g/wwwl/embl.org/htdocs/cmci/testnew/dw2/biii.info/sites/default/modules/contrib/biblio/biblio.module on line 1685\r\n</blockquote>\r\n\r\nTo fix this last point, I added a line in biblio.module to print the database loading process. This allowed to pinpoint the problematic node, which is then deleted, and biblio recovered in the website. In fact, the memory run out because of infinite looping due to unknown reason for that node.  \r\n\r\n', NULL, NULL, NULL, NULL),
('2243', NULL, '0', NULL, 'propagate (EBImage)', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('1656', NULL, '0', NULL, 'Differentials', 'Six 2D differential operations are implemented in this ImageJ plugin.\r\n\r\n- Gradient Magnitude\r\n- Gradient Direction\r\n- Laplacian\r\n- Largest Hessian\r\n- Smallest Hessian\r\n- Hessian Orientation', 'Philippe Thévenaz', NULL, 'http://bigwww.epfl.ch/thevenaz/differentials/', NULL),
('1750', NULL, '0', NULL, 'FastFilters3D', NULL, 'Thomas Boudier', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:3d_filters_with_jni:start', NULL),
('2419', NULL, '13', NULL, 'Automated velocity mapping of migrating cell populations (AVeMap)', 'Requires Matlab Runtime Environment or Matlab.\r\nSource code (m-files) are downloaded.\r\n\r\nSoftware availability:\r\nAVeMap was developed under MATLAB (MathWorks). It is available as an executable, multiplatform program, together with source codes and documentation at http://umr168.curie.fr/en/avemap, and the source code is also available as Supplementary Software. For practical reasons, this executable version, which does not require MATLAB, runs on a single processor. For users who want to customize the software and/or need the power of parallel computing, an installation of MATLAB with its \'parallel\' and \'image processing\' toolboxes is needed. Note that, even with the executable version, the velocity fields are stored for further analysis. The add-on AVeMap+ uses these AVeMap-computed velocity fields to generate heat map tables. It is available with the same link.', 'Maxime Deforet,	Maria Carla Parrini,	Laurence Petitjean,	Marco Biondini, Axel Buguin, Jacques Camonis,  Pascal Silberzan', NULL, NULL, NULL),
('2282', NULL, '11', NULL, 'RoadMap', '<p>A tentative roadmap of Biii.</p>\r\n\r\n<h2>Phase 0 (done)</h2>\r\n\r\n<ul>\r\n<li>Tagging using the platform based on Google Doc</li>\r\n<li>Decide platform.</li>\r\n<li>Construct the site with pages of image processing / analysis functions, packages and languages.</li>\r\n<li>Name the site (now, its "Biii")</li>\r\n<li>Tagging on each page is done in parallel.</li>\r\n</ul>\r\n\r\n<h2>Phase 1 (done)</h2>\r\n\r\n<img src="/files/biii/BiiiInformationFlow_0.png" width="400" height="300" alt=""  />\r\n\r\n<p>Discussion going on here at the moment <a href="https://docs.google.com/document/d/1rz_n1xEfvytMNOAbQShCVAe6PIirsJw-tx7CIyEd4og/edit">google doc, anonymous access allowed</a></p>\r\n\r\n<p><a href="https://docs.google.com/document/d/1fuDCt19OHbO0pYMp1kS1Lm_Y-wzh_YrD4M_sQp5m3zI/edit"> A Task list proposed by Sebastien is here</a></p>\r\n\r\n<ul>\r\n<li>always in progress/ Add more tags!</li>\r\n<li>done/ Collect feedbacks from various types of groups (e.g. cell biologists, developmental biologists, neurobiologists, bioimage analysts, image processing specialists). Especially those from biologists are required.</li>\r\n<li>done /Implement more direct tagging interface.</li>\r\n<li>done/ Implement module to add backlinks.</li>\r\n<li>done/ Prepare two types of interfaces: for none-experts and for experts.\r\n\r\n<ul>\r\n<li>For none-experts, workflow is better be presented as search results but for experts, functions would be convenient to have them as search results. Workflows are easier to use directly on bio-images but less flexible. Functions are flexible but difficult to use as it requires programming knowledge.</li>\r\n</ul>\r\n</li>\r\n</ul>\r\n\r\n<h2>Phase 2 (done)</h2>\r\n\r\n<ul>\r\n<li>Second round of Taggathon. More analysts to be invited.</li>\r\n<li>Reflect discussion results and proposed improvement during phase 1.</li>\r\n<li>Modules for avoiding spam bots are installed.</li>\r\n<li>back link modules added.</li>\r\n<li>biblio modules added for more efficient citation.</li>\r\n</ul>\r\nIssues leftover\r\n<ul>\r\n<li>always in progress/ Add more tags!</li>\r\n<li>in progress/ Ask biologists with image processing / analysis knowledge to tag.</li>\r\n<li>in progress / Analyze synonyms to improve search results.</li>\r\n<li>in progress/ Discuss if tags should be categorized.</li>\r\n</ul>\r\n\r\nMore ToDo\r\n<ul>\r\n<li>ontology.</li>\r\n<li>improve advanced search, more interactive</li>\r\n<li>Change skin!</li>\r\n</ul>\r\n\r\n\r\n<h2>Phase 3</h2>\r\n\r\n<ul>\r\n<li>Grand Opening.</li>\r\n<li>Taggathon will be periodically organized to improve biology - image analysis bridging (searching).</li>\r\n</ul>', NULL, NULL, NULL, NULL),
('2621', NULL, '85', NULL, 'OpenComet: an automated tool for comet assay image analysis.', NULL, NULL, NULL, NULL, NULL),
('2624', NULL, '79', NULL, 'Image Analysis Course for Life Science by Morten Larsen, University of Copenhagen', '**Image Analysis Course for Life Science Faculty.**    \r\nMaterial includes: slides, hands-on exercises using ImageJ and related data.   \r\nThere is a full lecture on ImageJ macro language (session 12).    \r\nWebsite: [http://www.matdat.life.ku.dk/ia/]   \r\n   \r\nSession 1: **The basics: Images** - Digital image representation, Image processing and image analysis, Concerns at image acquisition, Pixel-wise transformations, Visual enhancement   \r\n   \r\nSession 2: **Filtering and noise reduction** - What is filtering? , Noise and noise reduction, Convolution filters, Ranking filters,  Anisotropic smoothing, Background subtraction   \r\n   \r\nsession 3: **Colour (and multispectral) images** - Colour and colour spaces,  Colour imaging and calibration, Spectral unmixing / Colour deconvolution,  Colour thresholding, Principal component analysis   \r\n   \r\nSession 4: **Thresholding and binary images** - Thresholding,  Automatic threshold selection, Binary logic, Masking, Binary morphology, Euclidian distance transform   \r\n   \r\nsession 5-6: **Object level analysis** - From pixels to objects, Counting and characterizing objects, Skeleton and EDT   \r\n   \r\nsession 7: **The frequency domain** - The Fourier transform (1D), The Fourier transform (2D), Interpretation of frequency domain images, Modifications in the frequency domain, Convolution/multiplication duality, The inverse filter and the Wiener filter   \r\n   \r\nsession 8-9: **Derivatives, edges and texture** – Derivatives, Edge detection,  The Hough transform, Texture   \r\n   \r\nsession 10: **Template matching, alignment,interpolation and resampling** - Template matching, Correlation in the spatial domain, Image alignment, Correspondance matching, Interpolation, Resampling   \r\n   \r\nsession 11: **Classification and clustering**  \r\n   \r\nsession 12: **ImageJ macro programming** - ImageJ Macros, Image titles and IDs, Invoking macros and installing macro sets, Text output, Generalizing macros   \r\n', NULL, NULL, 'http://www.matdat.life.ku.dk/ia/', NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2625', NULL, '79', NULL, 'Image Processing, Visualization and quantification class by Sam Johnson, Duke University', 'Image Processing, Visualization and quantification class \r\n\r\nMixture of some lecture-format background information and practical computer exercises using Fiji, \r\n [slides](http://microscopy.duke.edu/learn/ImageAnalysis_1D.pdf) and [example images](http://microscopy.duke.edu/learn/Images.zip) from 1 day course. \r\n\r\n**Topics covered in the class include:**  \r\n  \r\nUsing and understanding microscope-specific image formats  \r\nBit depths and grey values  \r\nHistograms and digital contrast scaling  \r\nUse and limitations of color  \r\nMaking figures accurately  \r\n3D visualization - principles and basic operations  \r\nQuantification - Numbers, sizes, intensities and other basic analysis measurements...    \r\nUse of FIJI ImageJ to do all these things  \r\n(The class deals mainly with fluorescence images/micrographs rather than medical images, but the concepts and procedures are often similar)  ', NULL, NULL, 'http://microscopy.duke.edu/learn/image_analysis.html', NULL),
('2614', NULL, '11', NULL, 'ImageJ Plugin for Non-Local-Means Filtering', 'A more modern approach for denoising / smoothening before segmentation, works like Gaussian blurring but preserves edges and boundaries. Listed in Fiji update sites. \r\n\r\n## Algorithm\r\n\r\nAlgorithm description is in [this page](http://www.ipol.im/pub/art/2011/bcm_nlm/) <bib>2612</bib>.\r\n\r\n## Example usage\r\n\r\nLocalization of Membrane bound protein in Arabidopsis meristem was analyzed using the non-local-mean filter for refining its position <bib>2613</bib>. \r\n\r\n## impression\r\n\r\nIt\'s effect is somewhere between Gaussian blurring and anistropic diffusion.  \r\n\r\n', 'Thorsten Wagner, Pascal Behnel', NULL, 'https://github.com/thorstenwagner/ij-nl-means', NULL),
('2261', NULL, '11', NULL, 'EuBIAS', '<h1>EuBIAS Manifesto</h1>\r\n\r\n<p>Date: June 19, 2013</p>\r\n\r\n<p>Authors: Kota Miura, Sébastien Tosi, Julien Colombelli</p>\r\n\r\n<a href="http://dx.doi.org/10.5281/zenodo.18047"><img src="https://zenodo.org/badge/6611/cmci/EuBIAS_Manifesto.svg"><a>\r\n\r\n<h2>Introduction</h2>\r\n\r\n<p>Bioimaging is an interdisciplinary science. In addition to biology itself, a deep understanding of optics, microscopy, fluorescence probes, image processing and image analysis is required. Due to this overwhelming need of diverse technologies and since it is rather unlikely a single individual can master all these topics, biologists and imaging scientists need to work together to achieve higher scientific goals. In order to facilitate this collaborative process, a strengthening of the bioimaging infrastructure is required. In the following, we focus on the image processing and analysis in bioimaging. We first briefly review the current status of the infrastructure and then explain the need for actions to promote a higher accessibility to bioimage analysis for biological researchers.</p>\r\n\r\n<p>Among the overall bioimaging workflow, image processing and analysis come at the very last end. Microscope images are multidimensional data representing complex biological phenomena that involve biochemical reactions occurring within three dimensional spatial contexts. The major task of image processing and analysis is to computationally decrease the dimensionality of the problem under study, extract relevant quantitative information and conduct statistical data analysis.</p>\r\n\r\n<p>Compared to the other technologies involved in bioimaging, image processing and analysis is unique in that its infrastructure is essentially made of computational tools. Hardware such as server and client machines, data storages and networks are the physical infrastructure for computation but not the main focus of this meeting. In turn software and its intrinsic capability for simple dissemination is the main target.</p>\r\n\r\n<p>To organize and increase  in a scalable way the accessibility to image processing and analysis infrastructure within the European bioimaging community, its heavy dependence on human resource should be given a considerable attention.</p>\r\n\r\n<p>Software is a pure product of human imagination. Its availability is solely dependent on the effort of computational scientists and programmers who create algorithms and implement them. We call these individuals “developers”. They create image processing algorithms, design their efficient implementations allowing faster computations, and design graphical user interfaces to simplify the user interactions. The outcome of their efforts ranges from general algorithms that could be used as basis for many types of applications to specific solutions limited to certain biological phenomena. The developers are hence algorithm developers and programmers, but in many cases algorithm developers also take the role of programmers.</p>\r\n\r\n<p>As the number of image processing / analysis software and the diversity of image processing / analysis algorithms grew, professional knowledge to combine multiple image processing libraries and tools to construct complex workflows has increasingly become important. For this assembly task, insights in biology, image processing, various computational libraries, computer programming languages and statistics are necessary. Former developers were in many cases taking this role simultaneously, but recently a community of professionals specialized in assembling tools for such customized tasks is growing. They are mostly biologists, engineers and physicists and we will call this new type of professionals “analysts”.</p>\r\n\r\n<p>Existing software packages already make available a rich range of tools for processing and analysis of microscope images. They are however constantly evolving and the number of functions is steadily growing. With so many choices offered to the users, the problem to choose the best tool to answer a specific question is not trivial, and sometimes extremely complex. Many high end packages are open source and readily accessible but bridging that accessibility to a specific demand is missing. The enhancement and strengthening of an human network capable of boosting this optimization and allowing a better usage of the existing software through collaborations / consultations / teachings is equally important.\r\nTo organize and increase the accessibility to image processing and analysis infrastructure within the European bioimaging community in a scalable way, its heavy dependence on human resource itself should be given with a considerable attention.</p>\r\n\r\n<p>For this reason, we briefly overview the activities of developers and analysts. To assess the future of these activities, career paths will also be examined.</p>\r\n\r\n<h2>BioImage Community</h2>\r\n\r\n<h3>Developers</h3>\r\n\r\n<p>Image processing is a solid field where many algorithms have been developed to improve image quality, to extract features, to compress the information, to segment objects, to classify patterns, to track objects, to estimate model parameters in presence of noise and to interactively visualize the data. These advances have been made possible by the interactions between researchers in the field of signal processing, computer vision, mathematics, statistics, machine learning and computer science. Some of these researchers (developers) became specialized in treating microscope images in collaboration with biologists and contributed many software packages and tools (many are also lead by biologists).</p>\r\n\r\n<p>These contributions can be seen in a glance in the webpage of the <a href="http://www.openbioimage.org/">Open Bio Image Alliance (OBIA)</a>, as well as the developer on-going gatherings.</p>\r\n\r\n<p>Information exchange and human networks have been established through these meetings. The career path of developers inherits that of computer scientists and programmers. Computer scientists have their academic position in computer science, electrical engineering and signal processing departments or image processing position in biological / medical institutes. They publish their outcome of developments in computer science and signal processing journals as well as in biological journals as collaborators, which will be the credit for their scientific career.</p>\r\n\r\n<h3>Analysts</h3>\r\n\r\n<p>Analysts bridges the gap between developers and biologists. The gap exists since the demands of biologists could ultimately be summarized to "single click and does all". Such automation is possible for some trivial tasks but in many cases the complexity of modern Bioimage Analysis methods hinders the full automation. Every Bioimaging project is unique hence the image analysis, the final step of bioimaging, is also unique and cannot escape from some assembling and customizations. Here analysts come into play to create optimal protocols of image analysis.</p>\r\n\r\n<p>In addition to customization of image analysis protocols, another major activity of most of analysts is teaching. Teaching of basics on image processing and analysis techniques shifts up the level of “Image Processing and Analysis literacy” in their belonging institute, which collectively has positive effect in promoting high-level image analysis in that local Bioimaging community.</p>\r\n\r\n<p>The analyst is a new type of profession and their number is limited. The first precursory gathering of analysts took a form of course to teach image analysis to biologists in 2013 <a href="www.embl.de/bias2013">(EMBL Master Course on BioImage Data Analysis 2013, BIAS 2013)</a>. The prominent outcome of analysts working together was course textbooks written through collaborative editing and peer reviewing. Through this process, we learned creative ideas from others on how each has been solving various Bioimage Analysis problems. As each of us had been more-or-less working individually and locally, exchange of knowledge were highly valuable and enlightening.</p>\r\n\r\n<p>The background of analysts is diverse: Physicists, Computer Scientists, Electrical Engineers, Physical Chemists and Biologists. There is no established career path for becoming analysts, but a need of diverse knowledge as stated already. Compared to developers who have conventional criteria for evaluating their careers, analysts are currently evaluated only by words of mouth. An increased visibility of the their work is required both for stabilizing this new career path and at the same time for the employers to evaluate candidates.</p>\r\n\r\n<h3>Users</h3>\r\n\r\n<p>Scientists, engineers or students who, in collaboration with analysts or developers, or independently, perform BioImage processing &amp; analysis to ultimately answer scientific questions or support scientific and technological development.</p>\r\n\r\n<h2>Tasks</h2>\r\n\r\n<p>How could we increase the accessibility and diffusion of image processing / analysis infrastructures, especially in terms of software packages?</p>\r\n\r\n<p>Existing software packages already offer a rich and wide range of tools for processing and analysis of microscope images. They are constantly evolving and the number of new functions is steadily growing. With so many options offered to users, the problem for them is now to choose the best tool that matches their needs. In other words, the accessibility is already excellent, but bridging this accessibility to a specific demand is missing. Moreover, the enhancement and strengthening of a human network that can boost the diffusion of the existing software through collaborations / consultations / teachings is equally important.</p>\r\n\r\n<p>To overcome the imminent problem that exists as a gap between the availability of software tools and the difficulty of a specific choice, the flow of information could be smoothed from multiple angles. At the same time staging the role of “analysts” at the community level will be key. For these reasons, we propose the following actions:</p>\r\n\r\n<h3>A Web Tool</h3>\r\n\r\n<p>A list of links to software packages could be found in various sources, but from the user side this is not really practical because in actual research scenarios we want to find a specific function that matches the need regardless of the package in which it is contained. Many software packages are developed as a unit, so their documentations are monolithic. The list and associated documentations should become more functional rather than a one-dimensional listing of package names.</p>\r\n\r\n<p>A web platform that integrates all  documentations should be created. It could decrease perpetual “reinventions of the wheel” and may at the same time channel the new developments towards truly innovative extensions. We call such user-oriented functional and highly interactive web platform as a “web tool” and propose the following specifications:</p>\r\n\r\n<ol>\r\n<li>One page per Function\r\n\r\n<ol type="a">\r\n<li>Users try to find functions based on their needs rather than by name of the software package. This means that each function (plugin, menu item) included in the software packages should be assigned a single documentation page, and ideally should not be categorized by package.</li>\r\n<li>This is partially actualized in the Fiji project wiki page, where each contributed plugin features a page listing the name of the developer, the link to the source code and the usage instructions. This way of documentation should be extended to include all software packages and presented as single web document that offers higher accessibility to users.</li>\r\n</ol>\r\n</li>\r\n<li>Multiple tagging per Function\r\n\r\n<ol type="a">\r\n<li>A documentation page prepared for each function / modules / plugins / site-packages should have a tagging functionality. Tags are like keywords given to each page. Tags allow user to filter functions to find those that matches their needs.</li>\r\n<li>Each page should be added with <strong>multiple tags</strong>.</li>\r\n<li>Both developers and users should be able to create tags and add tags to any page.</li>\r\n<li>There should be “tag administrators”, who control the tagging behaviors and also promotes the tagging. Administrators themselves should commit in active tagging.</li>\r\n</ol>\r\n</li>\r\n<li>Back-links to literature\r\n\r\n<ol type="a">\r\n<li>In general, papers that uses image processing and analysis mention the name of the package and function that they used. This is so to say, a <strong>forward link</strong> to the tools from papers.</li>\r\n<li>Users often wants to know examples of usages. For this reason, having <strong>back-links</strong> to literature / Pubmed abstract page / websites using the function / tool is desirable so that users could be directed to actual example usages.</li>\r\n</ol>\r\n</li>\r\n<li>Comments from users\r\n\r\n<ol type="a">\r\n<li>A comment section should be added to each page so that any discussions / claims / questions could be viewed by other users.</li>\r\n<li>There could be further added with the aggregation of mailing list discussions (text scrapings).</li>\r\n<li>Access permissions (need more opinions on this issue):</li>\r\n<li>Similar to ‘Trip advisor’ (maybe hard to actualize), each function could feature a users’ and developers’ opinion chart together with the comments, about the various implementations of a function in different software packages or about the effectiveness of a function for a certain goal/analysis. So this is similar to a point developed above, but here the question is to discuss and decide whether to include a door for users to comment on the functions and their ease-of-use. Without an interactive access by users, the Wiki would lose the ‘wiki property’ which we look forward to developing, and could quickly be seen as a restricted portal where only a handful of developers can emit judgements. Obviously the comments page could be moderated by the developer and by other users with voting (‘like it’ / ‘don’t like it’).</li>\r\n</ol>\r\n</li>\r\n<li>sample datasets\r\n\r\n<ol type="a">\r\n<li>Links to sample datasets should be added to each page, so that user can try out the function and get a a feeling about the output to expect. As it is often difficult to know the limit of the functionality of tools, sample images will be a good reference.</li>\r\n<li>If possible, data storage and uploading features could be added. Then the sample data could be directly uploaded by taggers for users to test, evaluate and compare.</li>\r\n<li>When applicable ‘ground truth’ (expected) results of the function should ideally be added with each sample dataset.</li>\r\n</ol>\r\n</li>\r\n</ol>\r\n\r\n\r\n<p>A large part of the web tool should be edited manually. We propose a <strong>“taggathon”</strong> once the web tool proposed here becomes available. In this event, developers, analysts and users gather in one place and concentrate on tagging pages, back linking literature and adding documentation to each page.</p>\r\n\r\n<h3>Meetings</h3>\r\n\r\n<p>Meeting will be a place to collectively spread and absorb image processing and analysis activities. In terms of increasing the software packages / tools accessibility, three different types of meetings should be organized with clearly defined target groups (developers, analysts and users) and directed flow of information between these groups. As a byproduct meetings strengthen human networks that trigger individual communication to be smoother and more informal, which allows specific solutions to problems by connecting developers, analysts and users. We include teaching oriented activities, or courses, as meetings since although the format differs, we think that the functionality is common in terms of increasing the accessibility to software packages.</p>\r\n\r\n<h4>“Show Case” meetings (Developers and Analysts)</h4>\r\n\r\n<p>Software packages upgrade on daily basis and it is quite an effort to follow changes and new functions. An annual meeting with presentations explaining upgrades given by a developer from each software packages would be a great benefit for analysts to be updated with the latest changes. We call this a  “Show Case” meeting. In the same meeting, analysis would provide feed-backs by showing their usages, explaining merits and demerits of the software package and also to request missing functionalities. This meeting also invites prominent developers who are creating cutting edge algorithms so that the progress in the front line of image processing could also be known to developers and to analysts.</p>\r\n\r\n<h4>Image Analysis Courses (Analysts and Users)</h4>\r\n\r\n<p>In image analysis courses, analysts teach users on how to combine various tools to tackle practical problems, in order to narrow  the gap between software packages and user’s demands by increasing the image analysis literacy of users. The BIAS 2013 course could be a template for such courses.</p>\r\n\r\n<p>Several outcomes will be expected from this activity. The first is that users become more fluent with image analysis, which heightens their ability to choose and assemble appropriate tools. The second is that analysts learn from other analysts how they solved some specific problems and also possibly get aware of new problems they might be expected to solve in the future. The third is that by working together the community of analysts becomes strengthened, leading to an increase in lateral communications.</p>\r\n\r\n<p>Courses could target two different types of user group: the first group is graduate students, postdocs and research scientists. The teaching aims that they could reach a level to do basic analysis on their own. The second group is microscope facility staffs. As analysts are still missing in many places, microscope facility staffs are taking the role of analysts beside the maintenance of microscopes. For this group of people the course teaches more advanced contents and also provides and assists teaching in their own institutes (Meta-teaching).</p>\r\n\r\n<h4>General Assembly (Developers, Analysts, Users)</h4>\r\n\r\n<p>A general assembly is a gathering of all developers, analysts and users. During this gathering, hackathon, presentations and courses happen in parallel. This is because there are difference in interests among three different groups, but information flow between these groups could be efficiently increased by gathering at the same place. Analysts could teach users, and users could present their work and show how developers products are used, developers could report updates on their tools and libraries. In parallel the developers and analysts could also sit together in a hackathon to extend the tools. Analysts form the most capable group of people to feedback the developers with usages. More details on the merits and demerits of the general assembly could be further discussed.</p>\r\n\r\n<ul>\r\n<li>Developers sometimes go out from hackathon and give talks about their works. Discuss with analysts.\r\n\r\n<ul>\r\n<li>Hackathons are in many cases locally funded or in worst cases developers pay themselves. There should be more official support.</li>\r\n</ul>\r\n</li>\r\n<li>Analysts teach and also take a part in the Hackathon (when they are not teaching). Discuss with developers on the usages.</li>\r\n<li>Users acquire a deep knowledge on usage of tools from the developer talks. Also being taught  practical strategies from analysts. Users present their work.</li>\r\n</ul>\r\n\r\n\r\n<h3>Open Textbooks for BioImage Analysis</h3>\r\n\r\n<p>A significant problem of image analysis in biology is the lack of textbooks. For image processing, there are many textbooks written by computer scientists. These textbooks are extremely valuable as they explain the users the theory behind image processing algorithms and how they are working. However, these textbooks cover only a part of the task of image analysis in biology. In addition to image processing algorithms, bioimage analysis requires knowledge on what could be measured and how to measure biological images, but this part does not exist as textbook. This aspect of bioimage analysis is supposed to fulfill in biology a role similar to that of analytical chemistry in chemistry. As it has been missing, it turned out to be the fundamental reason for this growing gap between image processing software and user’s demands.</p>\r\n\r\n<p>To fill this gap, analysts are at work interactively to bridge biologist’s demands and the image processing world, but their work should be organized as textbooks for efficient and systematic propagation of knowledge and techniques. As a start-up BIAS 2013 course prepared original textbooks to cover the gaps and to solve the fundamental problem in the accessibility to the image processing and analysis infrastructure. The writing of these textbooks could be standardized and the textbooks published as open textbooks to provide valuable and systematic information to biologists. At the same time, the publishing of the textbooks gives a firm scientific credit to analysts, secure their activity and pave the newly appearing career path, which may further attract more people to commit in this direction.</p>\r\n\r\n<h3>Further Actions to be considered</h3>\r\n\r\n<p>Following is a list of actions not explained in the previous text but which will be considered in future.</p>\r\n\r\n<ol>\r\n<li>For interoperability, define a standard platform (could be ImageJ, Python, Icy or any other). The platform does not have to be unique but it would be good that these platforms support multiple language bindings “from scratch” (such as Python) and that the pool of functions (plugins) that is written can easily be included to any of the platforms.</li>\r\n<li>As a roadmap, a list of algorithms “to be implemented” could be prepared based on their relevance.</li>\r\n<li>Set up a framework for algorithm evaluation with ground truth images, these datasets could also be used as example datasets.</li>\r\n</ol>\r\n', NULL, NULL, NULL, NULL),
('2623', NULL, '85', NULL, 'comet assay analysis', 'This workflow will batch process a directory of images:\r\n- comets should be horizontally oriented, tails to the right (additional pre-step if needed top reach this requirement: Rotate images, Using ImageJ/Transform Image or TransformJ plugin for exemple)\r\n- then included in the plugin:\r\n              ->Uneven background correction\r\n              -> automatiic detection of comet shapes with outliers detection\r\n               -> automatic detection of head of comets (brightest region or profile analysis)\r\n              -> statistics aboit tails, heads and Olive moments\r\n- Manual correction is also available\r\nCan be run from Micro-Manager for live analysis\r\nSee  [bib]2621[/bib] for associated paper.', 'Gyori et al', NULL, NULL, NULL),
('2427', NULL, '469', NULL, 'LIF Projector', '<blockquote>This macro is similar to the <a href="http://biii.info/node/2425">LIF_Extractor</a>, but it will output Z-projection of each Z-stack. You can choose the type of projection in addition to the other options.</blockquote>\r\n\r\n\r\n<em>Requires <a href="http://loci.wisc.edu/software/bio-formats">Bio-Formats</em> plugin', 'Christophe Leterrier', NULL, NULL, NULL),
('2626', NULL, '11', NULL, 'GDSC Single Molecule Localisation Microscopy', 'The GDSC Single Molecule Light Microscopy (SMLM) plugins is a package of tools for single molecule localisation analysis. \r\n\r\n- Fitting Plugins: get point cloud from super resolution image. \r\n- Results Plugins: organize results. \r\n- Analysis plugins\r\n- Model plugins\r\n\r\n', 'Alex Herbert', NULL, 'http://www.sussex.ac.uk/gdsc/intranet/microscopy/imagej/smlm_plugins', NULL),
('2523', NULL, '426', NULL, 'FISH-quant', 'Matlab toolbox to analyze single molecule mRNA FISH data. Allows counting the number of mature and nascent transcripts in 3D images. See <bib>2513</bib>.\r\n\r\nFollowing toolboxes are required:\r\n-  Optimization toolbox\r\n-  Statistics toolbox\r\n-  Image processing toolbox\r\n-  (Optional) Parallel processing toolbox', 'Florian Mueller', NULL, NULL, NULL),
('2628', NULL, '11', NULL, 'A self-organized biomechanical network drives shape changes during tissue morphogenesis.', NULL, NULL, NULL, NULL, NULL),
('2629', NULL, '11', NULL, 'CellTrack: an open-source software for cell tracking and motility analysis', NULL, NULL, NULL, NULL, NULL),
('2627', '2627', '11', NULL, 'CellTrack ', 'A standalone cell tracking software for single cell migration <bib>2629</bib>. \r\nTracking of cells in tissue was also done in Drosophila germband <bib>2628</bib>.\r\n\r\n**Download (or make in Linux)**\r\n\r\nSee <http://bio.cse.ohio-state.edu/CellTrack/> ', 'Ahmet Sacan, Hakan Ferhatosmanoglu, and Huseyin Coskun', NULL, 'http://bio.cse.ohio-state.edu/CellTrack/', NULL),
('2630', NULL, '11', NULL, 'Phase locking and multiple oscillating attractors for the coupled mammalian clock and cell cycle', NULL, NULL, NULL, NULL, NULL),
('2631', NULL, '11', NULL, 'Extracting Fluorescent Reporter Time Courses of Cell Lineages from High-Throughput Microscopy at Low Temporal Resolution', NULL, NULL, NULL, NULL, NULL),
('2616', NULL, '11', NULL, 'ImageJ Plugin Ridge Detection', 'A convenient tool for detecting lines!\r\n\r\nAfter the detection, detected lines are overlaid to the image. The plugin also stores these lines as ROIs, which could then easily be analyzed as vector information. \r\n\r\nInstead, the list of coordinates of all detected lines are placed in "contour" table. This could be used for redrawing or converting them as arrays. \r\n\r\n## Algorithm\r\n\r\nSee <bib>2615</bib>.', 'Thorsten Wagner', NULL, 'http://fiji.sc/Ridge_Detection', NULL),
('2632', NULL, '11', NULL, 'ImageJ Plugin LineageTracker', '## Short Summary\r\n\r\nQuote from the plugin page:\r\n\r\n>LineageTracker offers an ImageJ based framework which is easily extendible and has the capability to track cell lineages while being specifically designed to handle large cell displacements between frames. The methods are designed for fluorescent cells and have been used to analyse Schizosaccharomyces pombe, C2C12 mouse stem cells or migrating RPE cells.\r\n\r\nThis tool also allows flexible cell segmentation and extendable in all aspects. The webpage is detailed with usage from ImageJ macro. Rather than being simply a component, the plugin is indeed a framework with set of components.   \r\n\r\n## Misc info\r\n\r\nA tip from the plugin author in ImageJ mailing list (08.Sep.2015): \r\n\r\n> We have an additional script to export only a selected range of frames. I can send you that if you think LineageTracker is something for you.\r\nTo be on the safe side I would try it with an older version of ImageJ. We have experienced some problems, mostly related to Java. Java 8 seems to fix most of it.\r\n\r\n## References\r\n\r\n<bib>2630</bib>: Application example. \r\n\r\n<bib>2631</bib>: Plugin Paper. \r\n\r\n', 'Downey MJ, Jeziorska DM, Ott S, Tamai TK, Koentges G, Vance KW, Bretschneider T.', NULL, 'http://www2.warwick.ac.uk/fac/sci/systemsbiology/staff/bretschneider/lineagetracker/', NULL),
('2544', NULL, '93', NULL, 'idTracker: Tracking animals', '<blockquote>idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general.</blockquote> Technique details and analyses of several applications are described in <bib>2607</bib>.\r\n\r\nVideo protocol: [https://www.youtube.com/watch?v=oC9tp5TKAyw](https://www.youtube.com/watch?v=oC9tp5TKAyw)', 'A. Pérez-Escudero, J. Vicente-Page, R.C. Hinz, S. Arganda, G.G. de Polavieja', NULL, NULL, NULL),
('1531', NULL, '0', NULL, 'Gaussian Convolution', NULL, NULL, NULL, 'http://www.bioconductor.org/packages/release/bioc/manuals/EBImage/man/EBImage.pdf', NULL),
('2414', NULL, '469', NULL, 'Comet assay/DNA damage assay ', '<blockquote>This is a simple example of a DNA damage assay using single cell gel electrophoresis. Here, the measurement of interest is the length and intensity of the comet tail. Also, illumination correction is used to reduce background flourescence prior to measurement. Also shown is a silver-stained comet example in which the percentage of DNA contained in the tail is calculated.</blockquote>', 'A Great Guy', NULL, NULL, NULL),
('2633', NULL, '11', NULL, 'Advanced Form Block Patching', '[Advanced Form Block](https://www.drupal.org/project/afb) was recently updated, and I had to patch this module again for associating tag-editing block with its belonging node. \r\n\r\nThe patch is:\r\n\r\n[Allow edit block based on current node](https://www.drupal.org/node/2005086)\r\n\r\nThis time, patching was not straightforward as there was an error:\r\n\r\n    afb miura$ patch -p1 < 0001-allow-for-auto-detection-of-node.patch\r\n    patching file afb.module\r\n    Hunk #2 FAILED at 209.\r\n    Hunk #3 succeeded at 244 with fuzz 1 (offset 2 lines).\r\n    Hunk #4 succeeded at 683 with fuzz 2 (offset 7 lines).\r\n    Hunk #5 succeeded at 791 (offset 7 lines).\r\n    Hunk #6 succeeded at 814 (offset 7 lines).\r\n    1 out of 6 hunks FAILED -- saving rejects to file afb.module.rej\r\n\r\n\r\nThe rejected hunk was:\r\n\r\n\r\n	***************\r\n	*** 180,187 ****\r\n	   * Returns the page containing existing block list with block creation form.\r\n	   */\r\n	  function afb_admin_page() {\r\n	-   $output = render(drupal_get_form(\'afb_existing_blocks_display_form\'));\r\n	-   $output .= render(drupal_get_form(\'afb_new_block_create_form\'));\r\n	  \r\n	    return $output;\r\n	  }\r\n	--- 209,218 ----\r\n	   * Returns the page containing existing block list with block creation form.\r\n	   */\r\n	  function afb_admin_page() {\r\n	+   $form = drupal_get_form(\'afb_existing_blocks_display_form\');\r\n	+   $output = render($form);\r\n	+   $form = drupal_get_form(\'afb_new_block_create_form\');\r\n	+   $output .= render($form);\r\n	  \r\n	    return $output;\r\n	  }\r\n\r\nand the current corresponding lines are \r\n\r\n	/**\r\n	 * Returns the page containing existing block list with block creation form.\r\n	 */\r\n	function afb_admin_page() {\r\n	  $output = array(\r\n	    \'existing_blocks\' => drupal_get_form(\'afb_existing_blocks_display_form\'),\r\n	    \'create_new_block\' => drupal_get_form(\'afb_new_block_create_form\'),\r\n	  );\r\n	\r\n	  return $output;\r\n	}\r\n\r\n\r\nAs it seems to be OK with this, I left the rejected part as it is. The patch file is saved in the afb module root (biii_info/sites/default/modules/contrib/afb/).', NULL, NULL, NULL, NULL),
('2013', NULL, '0', NULL, 'Smooth', 'This CellProfiler module allows smoothen the image with a choice from various algorithms: \r\n\r\n- Fit Polynomial\r\n- Gaussian Filter\r\n- Median Filter\r\n- Bilateral Filter\r\n- Circular averaging\r\n- "Smooth to Average" filter. ', NULL, NULL, 'http://www.cellprofiler.org/CPmanual/Smooth.html', NULL),
('2634', NULL, '11', NULL, 'FibrilTool, an ImageJ plug-in to quantify fibrillar structures in raw microscopy images.', NULL, NULL, NULL, NULL, NULL),
('2635', NULL, '11', NULL, 'FibrilTool', 'Evaluates the orientation of fiber orientation pattern and plots the results in the image. \r\n\r\nThe workflow\r\n\r\n- calculates gradient in x and y direction. \r\n- then calculates the eigenvector of nematic tensor, which is the orientation of the pattern. \r\n\r\n<bib>2634</bib>', 'Arezki Boudaoud', NULL, NULL, NULL),
('2361', NULL, '138', NULL, 'Multi Kymograph', 'This macro and plugins suite for ImageJ (and Fiji) serves to measure velocity of moving structures and visualize them, from image time series (2D over time).\r\nThe module can be installed in imageJ as a Macro Menu and each function/component can be called separately. \r\nThe full workflow consists in calling some, or all, the functions sequentially in order to get from the image preparation (e.g. filtering and visualization of tracks) to the production of the kymographs (time vs. distance plot) and their analysis (retrieving the velocities).\r\nHere is the full workflow sequence:\r\n\r\n- Load image sequence\r\n- Crop and time-filter the image sequence ("Walking average" plugin)\r\n- Generate tracks by z-projection ("Stack difference" plugin) \r\n- Select tracks and restore them in the original stack.\r\n- execute plugin "multiple kymograph"\r\n- Analyse: select edges of moving tracks graphically and quantify movement in a table.\r\n\r\ninput:   8-bit, 16-bit stacks, 2D in time. Calibrated is better for meaningful velocity measurements.\r\nouput:  the kymograph image, the velocity measurements tables.\r\nrequires imagej version: 1.33.n minimum.\r\n\r\nExample of applications: velocity of moving objects/ strutures with sharp edges, incl.\r\nvelocity of microtubules (and their plus ends),\r\nvelocity of vesicles or particles along a 2D path,\r\nvelocity of migration of the edge of a cell or a multicellular group, \r\nretraction velocity of contractile bundles (e.g. actin fibers) or multicellular tissues after mechanical disruption (e.g. laser surgery)\r\n ', 'Jens Rietdorf and Arne Seitz', NULL, NULL, NULL),
('2636', NULL, '11', NULL, 'Statistical analysis of 3D images detects regular spatial distributions of centromeres and chromocenters in animal and plant nuclei.', NULL, NULL, NULL, NULL, NULL),
('1785', NULL, '0', NULL, 'Spatial statistics 2D/3D ImageJ plugin', '>The plugin analyses a point pattern (positions of objects of interest) distributed within a reference structure. This analysis allows in particular to assess deviation from spatial randomness, and to reveal trends for clustering (attraction) or regularity (repulsion). No edge correction is performed, as it is assumed that no point is expected outside the reference structure.\r\n\r\nSee <bib>2636</b> for the original article. \r\n\r\n![F-function plot](http://imagejdocu.tudor.lu/lib/exe/fetch.php?cache=&media=plugin:stacks:3d_ij_suite:fhisto.png)\r\n\r\nThis plugin comes together with [3D ImageJ suites plugin](http://biii.info/node/1945).\r\n\r\n ', 'Philippe Andrey, Thomas Boudier', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:spatial_statistics_2d_3d:start', NULL),
('2637', NULL, '11', NULL, 'Angiogenesis Analyzer for ImageJ', '>The "Angiogenesis Analyzer"  allows analysis of cellular networks. Typically, it can detect and analyze the pseudo vascular organization of endothelial cells cultured in gel medium.\r\n\r\n>...a simple tool to quantify the ETFA (Endothelial Tube Formation Assay) experiment images by extracting characteristic information of the network.\r\n\r\n[The outputs are network feature parameters.](http://image.bio.methods.free.fr/ImageJ/?Angiogenesis-Analyzer-for-ImageJ&lang=en&artpage=6-7#outil_sommaire_5)', 'Gilles Carpentier', NULL, NULL, NULL),
('2638', NULL, '11', NULL, '3D reconstruction of histological sections: Application to mammary gland tissue', NULL, NULL, NULL, NULL, NULL),
('1976', NULL, '0', NULL, 'AnalyzeSkeleton', '>This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length. The tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See [Skeletonize3D](http://imagej.net/Skeletonize3D) for an example of how to produce skeleton images.\r\n>\r\n>The voxels are classified into three different categories depending on their 26 neighbors:\r\n> - End-point voxels: if they have less than 2 neighbors.\r\n>- Junction voxels: if they have more than 2 neighbors.\r\n>- Slab voxels: if they have exactly 2 neighbors.\r\n>\r\n>End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.\r\n>\r\n>Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other. \r\n\r\nOutput data type: table result, image of the skeleton\r\n\r\n![image](http://imagej.net/_images/e/ea/Tagging_example.png)\r\n\r\nReference: <bib>2638</bib>.\r\n\r\n', 'Ignacio Arganda-Carreras', NULL, 'http://fiji.sc/AnalyzeSkeleton', NULL),
('2639', NULL, '11', NULL, 'Skeletonize', 'ImageJ native "Skeletonize" implementation. \r\n\r\n- works only with 8-bit binary image. \r\n\r\nA faster implementation is available as a plugin [Skeletonize3D](http://biii.info/node/1832) written by Ignacio Arganda-Carreras. Pros of this plugin is [summarized here](https://imagej.nih.gov/ij/docs/guide/146-29.html#infobox:Skeletonize-vs-Skeletonize3D).', 'Wayne Rasband', NULL, 'https://github.com/imagej/imagej1/blob/master/ij/process/BinaryProcessor.java#L101-L131', NULL),
('1832', NULL, '0', NULL, 'Skeletonize3D', 'For post-processing analysis, use [Analyze Skeleton](http://biii.info/node/1976) plugin written by the same author. ', 'Ignacio Arganda-Carreras ', NULL, 'http://fiji.sc/Skeletonize3D', NULL),
('1926', NULL, '0', NULL, '3D Objects Counter', NULL, 'Fabrice Cordelires, Jonathan Jackson', NULL, 'http://fiji.sc/3D_Objects_Counter', NULL),
('2620', NULL, '11', NULL, 'FeatureJ Derivatives', '\r\n## Algorithm \r\n\r\nSee <http://www.mif.vu.lt/atpazinimas/dip/FIP/fip-Derivati.html>.', 'Erik Meijering', NULL, 'http://www.imagescience.org/meijering/software/featurej/derivatives/', NULL),
('2609', '2609', '424', NULL, 'VolViewer', '# Summary\r\nVolViewer is used for viewing volume images from, for example, confocal microscopy or optical projection tomography\r\n\r\n# Features\r\n*   Real-time volume rendering using an optimized 3D texture slicing algorithm.\r\n*   Interactive transfer functions to independently adjust opacity and intensity for up to three data channels.\r\n*   Real-time per channel thresholding, brightness and contrast operators.\r\n*   On-the-fly gradient computation for local illumination.\r\n*   Iso-surface computation with surface smoothing.\r\n*   Section viewing in any orientation / position.\r\n*   Real-time volume clipping.\r\n*   3D measurements, filters & segmentation.\r\n*   Key frame interpolation for movie export.\r\n*   Stereo rendering using either quad buffer or anaglyph mode.\r\n*   Scripting interface to other systems, e.g. Matlab, OMERO, etc. \r\n\r\n# Project Status\r\n*  Not supported anymore\r\n\r\n# Source code\r\n*  [Source code](https://github.com/ut666/VolViewer "GitHub repository")', 'Jerome Avondo', NULL, 'http://cmpdartsvr3.cmp.uea.ac.uk/wiki/BanghamLab/index.php/VolViewer', NULL),
('2583', '2583', '92', NULL, 'PRIISM/IVE', 'Priism is an image processing, analysis and visualisation application for multidimensional light and electron microscopy data. It uses the Image Visualization Environment (IVE) library, which is a closed-source C library with documented C and FORTRAN APIs. Priism/IVE was developed at UCSF, and can be obtained free of charge by sending an email request to the developers.\r\n\r\nMany of the same processing and analysis functions available in other image processing applications can be found here (cropping, resizing, spatial filters, Fourier transforms, segmentation etc.), and it includes a comprehensive html-based offline manual. The "SoftWoRx" package of GE Healthcare (formerly Applied Precision) was based on Priism/IVE.', 'IVE development team', NULL, 'http://msg.ucsf.edu/IVE/', NULL),
('2554', NULL, '137', NULL, 'Correct 3D drift', 'Rigid registration of time series in 3D.\r\n\r\nA video tutorial is available (be careful of sounds, the video automatically starts!): [Sample Drift Correction Following 4D Confocal Time-lapse Imaging](http://www.jove.com/video/51086/sample-drift-correction-following-4d-confocal-time-lapse-imaging)', ' Albert Cardona and Robert Bryson-Richardson', NULL, 'http://fiji.sc/Correct_3D_drift', NULL),
('2536', NULL, '137', NULL, 'Measure cell volume over time', 'The workflow describes a solution to measure growth of cells in 3D. \r\n\r\nThe sample dataset (part of the github repository) contains 2-Photon images of neurons. The neurons are imaged in 3D at two timeframes.To be able to measure significant differences in cell volume, the time gap of the frames is huge (ca. 30 min) and the animal was removed in the waiting phase. Thus, there is significant shift between the frames that has to be corrected before cell detection and tracking.  \r\n\r\nThe worflow consists of following steps:\r\n\r\n1. Import of single tiff slices [imageJ macro]\r\n2. Organizing the data in a 4D time series with 2 time frames [imageJ macro]\r\n3. Correction of shift between the time frames by rigid registration [imagJ macro]\r\n4. Bleaching correction [imageJ macro]\r\n5. Export of preprocessed image data in ics/ids format [imageJ macro]\r\n\r\n6. Import of ics/ids data to Imaris [Imaris]\r\n7. Cell object detection as "Imaris Surface Object" [Imaris]\r\n8. Tracking cell objects over time [Imaris]\r\n9. Split Tracks (use Imaris XT extension "Split Tracks") to generate single cell objects [Imaris]\r\n10. Export the statistics: Select the complete folder, go to the statistics tab and use ‚Full Export’ [Imaris]\r\n\r\nThe prepocessing macro can be referenced here: [![DOI](https://zenodo.org/badge/7683/cmohl2013/registration_and_bleaching_correction.svg)](http://dx.doi.org/10.5281/zenodo.13167)\r\n\r\nThe sample images were acquired by Cordula Ulbrich (Petzold Group at German Center of Neurodegenerative Disesases (DZNE), Bonn, Germany).\r\n\r\n\r\n', 'Imaris Tutorials, Christoph Moehl', NULL, NULL, NULL),
('2530', '2530', '94', NULL, 'CellSegm', 'An automated MATLAB tool for segmentation of surface stained cells', 'Erlend Hodneland', NULL, 'https://github.com/ehodneland/cellsegm/', NULL),
('1468', NULL, '0', NULL, 'Analyze Skeleton 2D time series', '\r\nThis is a scripting example to process stack of images using [AnalyzeSkeleton3D](http://biii.info/node/1976)', NULL, NULL, 'http://fiji.sc/Analyze_Skeleton_2D_time_series', NULL),
('2640', '2640', '138', NULL, 'Labview', 'A commercial software traditionally used in Industry and Engineering/Science to enable fast software development and deployment of a very broad range of devices control. Labview enables graphically oriented programming (no text-based coding) and offers many ready-made tools to perform basic tasks on complex data (including image data), maths operation, data handling and representation. \r\nFor Image processing and analysis, Labview offers the integrated "NI Vision" tool, used in image-based quality control of production lines with a broad selection of Image-based filters/operations.\r\nIn microscopy, Labview can be used efficiently to perform any kind of instrument control, and in particular "Feedback Microscopy" (also called intelligent microscopy, etc...) where the live analysis of a captured image will update the target of the microscope to make it understand where to image efficiently. ', 'National Instruments', NULL, 'http://www.ni.com/labview/', NULL),
('2346', NULL, '11', NULL, 'How to add a component / workflow page', 'Before adding a tool/function/implementations/scripts, you should decide if it is a **component or workflow**. If you are not sure with these terms, [please first read here.](http://biii.info/HowTo). A collection of components, without specific workflow, is called **software** including library. \r\n\r\n## To add a component page:\r\n\r\nComponent page is for \r\n\r\n- a menu item in GUI (such as plugin, e.g. "ManualTracking" in Fiji) \r\n- a single command in CUI (e.g. r.watershed in R) \r\n\r\n###Step-by-step instruction\r\n\r\n1. In the left navigation panel, click "Add Content". "Add Content" panel opens. \r\n2. Click "Component". \r\n3. Fill following fields:\r\n   1. Title (required): name of the menu item or the command\r\n   2. Tags: Tags that describe the function of the item. If the title is "watershed" it could be something like "segmentation", "overlaps", "morphology" and so on. tags can be edited afterwards, so don\'t be too cautious. More tags are better than less. \r\n   3. URL: Links to the web page of this item (if this item is ImageJ manual tracking, it will be <http://rsb.info.nih.gov/ij/plugins/track/track.html>)\r\n   4. Authors: Not you, but the author of the item. If this item is  ImageJ manual tracking, then "Fabrice Cordelieres"\r\n   5. Description: Write in text what this component does.\r\n   6. Workflow: workflow page in the BIII that uses this component. \r\n   7. Package / Library: Choose a package that this component is included. You could not find one from the list, please add a software / library page. It will then appear automatically in the list. \r\n4. Preview button is at the bottom. Check the preview, and if everything looks ok, please "Save". \r\n\r\n## To add a workflow page:\r\n\r\nWorkflow pages are for\r\n\r\n- Practical workflow starting from a microscope image to analysis results. \r\n- We recommend only links and short description to external site, not to write the whole workflow directly in BIII. \r\n\r\n###Step-by-step instruction\r\n\r\n1. In the left navigation panel, click "Add Content". "Add Content" panel opens. \r\n2. Click "Workflows". \r\n3. Fill following fields:\r\n   1. Workflows (required): name of the workflow\r\n   2. Tags (required): Tags that describe what this workflow does.\r\n   3. Workflow URL: Links to the web page of this item. \r\n   4. Example Image URL: Link to the example image for testing the workflow. \r\n   5. Comment/ Instructions: Please explain what this workflow does. It will be very helpful which organism is the target. (It could be "general" as well).\r\n   6. Workflow Authors: Not you, but the author of the workflow. \r\n   7. Description: Write in text what this component does.\r\n   8. Workflow: workflow page in the BIII that uses this component. \r\n   9. Dependencies: Select one or more software/libraries that is required for the workflow. \r\n   10. Language: if the workflow is in a form of a macro or script, please select a language. If there is none, you do not have to choose, or you could write about it in "comment/instructions" about the special programming language that is used. \r\n4. Preview button is at the bottom. Check the preview, and if everything looks ok, please "Save". ', NULL, NULL, NULL, NULL),
('2296', '2296', '11', NULL, 'BioImageXD', '<p>BioImageXD is a free open source software package for analyzing, processing and visualizing multi-dimensional microscopy images. It\'s a collaborative project, designed and developed by microscopists, cell biologists and software engineers from the Universities of Jyväskylä and Turku in Finland, Max Planck Institute CBG in Dresden, Germany and collaborators worldwide. BioImageXD was published in the July 2012 issue of Nature Methods.</p>', NULL, NULL, 'http://www.bioimagexd.net/', NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2641', '2641', '566', NULL, 'Cytomine', 'Cytomine is a rich internet application using modern web and distributed technologies (Grails, HTML/CSS/Javascript, Docker), databases (spatial SQL and NoSQL), and machine learning (tree-based approaches with random subwindows) to foster active and distributed collaboration and ease large-scale image exploitation. \r\n\r\nIt provides remote and collaborative principles, rely on data models that allow to easily organize and semantically annotate imaging datasets in a standardized way (using user-defined ontologies associated to regions of interest),\r\nefficiently support high-resolution multi-gigapixel images (incl. major digital scanner image formats), and provide mechanisms to readily proofread and share image quantifications produced by any image recognition algorithms. \r\nBy emphasizing collaborative principles, the aim of Cytomine is to accelerate scientific progress and to significantly promote image data accessibility and reusability. Cytomine allows to break common practices in this domain where imaging datasets,\r\nquantification results, and associated knowledge are still often stored and analyzed within the restricted circle of a specific laboratory.\r\n\r\nThis software is e.g. being used by life scientists to help them better evaluate drug treatments or understand biological processes directly from whole-slide tissue images, by pathologists to share and ease their diagnosis, and by teachers and students for pathology training purposes.', 'Raphaël Marée, Loïc Rollus, Benjamin Stévens, Renaud Hoyoux, Gilles Louppe, Rémy Vandaele, Jean-Michel Begon, Philipp Kainz, Pierre Geurts and Louis Wehenkel', NULL, 'http://www.cytomine.be/', NULL),
('2275', '2275', '2', NULL, 'ilastik', '<p>ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows.  ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension).  Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.  The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.</p>\r\n\r\n<p>Other workflows include:\r\n<ul>\r\n<li>Object classification: Similar to pixel classification, but classifies previously segmented objects instead of making pixel-wise decisions</li>\r\n<li>Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds</li>\r\n<li>Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations</li>\r\n<li>Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning</li>\r\n<li>Density Counting: Learned cell population counting based on interactively provided user annotation</li>\r\n</ul></p>\r\n\r\n<p><b>Strengths:</b> interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source</p>\r\n<p><b>Weaknesses:</b> Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation</p>\r\n\r\n<p><b>Supported Formats:</b> hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif</p>', NULL, NULL, 'http://www.ilastik.org/', NULL),
('2642', '2642', '93', NULL, 'AIR tools', 'AIR Tools is a MATLAB software package for tomographic reconstruction (and other imaging problems) consisting of a number of algebraic iterative reconstruction methods. ', NULL, NULL, 'http://www.imm.dtu.dk/~pcha/AIRtools/', NULL),
('2602', NULL, '11', NULL, 'Tubeness', 'Morphometry of "Tubeness" using metrics devised by Sato et al. <bib>2601</bib>.', 'Mark Longair, Stephan Preibisch, Johannes Schindelin.', NULL, 'http://www.longair.net/edinburgh/imagej/tubeness/', NULL),
('2644', NULL, '587', NULL, 'MosaicIA', 'MosaicIA is a tool to analyze the spatial distribution of objects in images. It estimates from an observed particle or object distribution what hypothetical interaction between the objects is most likely to have created this distribution.', 'A. Shivanandan, A. Radenovic, and I. F. Sbalzarini', NULL, 'http://imagej.net/MosaicIA', NULL),
('2645', '2645', '587', NULL, 'Lama: The LocAlization Microscopy Analyzer', 'LocAlization Microscopy Analyzer (LAMA) is a software tool that contains several well-established data post processing algorithms for single-molecule localization microscopy (SMLM) data. LAMA has implemented algorithms for cluster analysis, colocalization analysis, localization precision estimation and image registration. LAMA works with a graphical user interface (GUI), and accepts simple input data formats as supported by various single- molecule localization software tools.', 'Sebastian Malkush, Mike Heilemann', NULL, 'http://user.uni-frankfurt.de/~malkusch/lama.html', NULL),
('2646', NULL, '587', NULL, 'ABSnake', 'ABSnake can segment complex structures in 2D images as well as 3D or temporal images. It uses a new active contour model based on a geometrical approach for correctly following invaginated structures.', 'P. Andrey, T. Boudier', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:segmentation:active_contour:start', NULL),
('2647', NULL, '587', NULL, 'Hough Circles', 'This plugin applies the Hough Transform for Circles to an 8-Bit image, shows the resulting Hough Space in a new window and marks the centers of the found circles.', 'Hemerson Pistori, Eduardo Rocha Costa', NULL, 'https://imagej.nih.gov/ij/plugins/hough-circles.html', NULL),
('2493', NULL, '472', NULL, 'Lymphatic Vessel Density calculation', 'This article <bib>2610</bib> present a method to compute Lymphatic Vessel Density on an image of the whole slide.\r\n\r\nVessels are obtained with a Maximum Entropy Thresholding applied on the excess Red channel (2 times the red values minus blue+green value).\r\nStroma tissue is obtained with a Moment Preserving Thresholding on the blue channel.', 'Cédric Balsat, Nicolas Signolle', NULL, NULL, NULL),
('2300', '2300', '11', NULL, '3D Slicer', 'Slicer, or 3D Slicer, is a free, open source software package for visualization and image analysis. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac Os X.', 'Steve Pieper, Ron Kikinis', NULL, 'http://www.slicer.org', NULL),
('1664', NULL, '0', NULL, 'PureDenoise', 'Outline\r\nThe incessant development of improved microscopy imaging techniques, as well as the advent of highly selective fluorescent dyes has made possible the precise identification of tagged molecules in almost any biological specimen. Of particular interest are the visualization and the study of living cells, which induce tight constraints on the imaging process. To avoid the alteration of the sample and to achieve a high temporal resolution, low fluorophore concentrations, low-power illumination and short exposure time need to be used in practice. Such restrictions have a tremendous impact on the image quality. This is why we have recently introduced a new method, coined PURE-LET [1,2,3], for efficient, fast, and automatic denoising of multidimensional fluorescence microscopy images.', NULL, NULL, 'http://bigwww.epfl.ch/algorithms/denoise/', NULL),
('2382', NULL, '470', NULL, 'Analyzing ER, PR, and Ki-67 immunohistochemistry', 'ImmunoRatio is an ImageJ plugin to quantify haematoxylin and DAB-stained tissue sections by measuring the percentage of positively stained nuclear area (labeling index), described in [bib]2452[/bib].\r\n\r\nNotes for use:\r\n\r\n* It is important to read the URL instructions and original paper to understand what is being measured.  In particular, the primary measurement made is *percentage of the total nuclear area*, not the *percentage of detected nuclei* (the latter being the more common method of assessing e.g. Ki67).  This may be further modified by the \'Result correction equation\'.\r\n* Ultimately ImmunoRatio relies on thresholding (color deconvolved [bib]2451[/bib]) images to define \'nucleus\' vs \'non-nucleus\' regions according to staining intensity.  Therefore dark artefacts, such as tissue folds, are likely to cause errors.\r\n* The pixel size is not read automatically from the image, but rather the *source image scale* should be entered into the dialog box - and the image rescaled accordingly prior to analysis. This scale value is the *inverse* of the value normally found for *pixel width* and *pixel height* under *Image -> Properties...* (i.e. pixel width & height are given in microns per pixel; the dialog box asks for pixels per micron).', 'Vilppu Tuominen & Jorma Isola', NULL, NULL, NULL),
('2648', NULL, '591', NULL, 'iterative closest point registration', 'The ICP algorithm takes two point clouds as an input and return the rigid transformation (rotation matrix R and translation vector T), that best aligns the point clouds.\r\n\r\nExample:\r\n[R,T] = icp(q,p,10);\r\n\r\nAligns the points of p to the points q with 10 iterations of the algorithm.\r\nThe transformation is then applied using\r\nR*p + repmat(T,1,length(p));\r\n\r\nThe file has implemented both point to point and point to plane as well as a couple of other features such as extrapolation, weighting functions, edge point rejection, etc.', ' Jakob Wilm, Hans Martin Kjer ', NULL, 'http://www2.imm.dtu.dk/~jakw/publications/bscthesis.pdf', NULL),
('2649', NULL, '138', NULL, 'BiDiFuse', 'to be completed', 'Jan Detrez et al', NULL, 'https://github.com/JanDetrez/BiDiFuse/', NULL),
('2314', '2314', '137', NULL, 'NeuronStudio', 'Neuron studio is a software package to reconstruct neurons from 3D confocal images. Reconstruction can be done manually, semi-manually or fully automatic. The images as well as the detected objects are rendered in 3D. \r\nA spine detection and classification function is also included. Results can be exported as a text file with coords of the spines.\r\n\r\nIt seems that active development has stopped in 2009.\r\nNeuronStudio is being developed at the Computational Neurobiology and Imaging Center (CNIC), a research laboratory at the Neuroscience Department of the Mount Sinai School of Medicine in New York. ', '     Susan L. Wearne Principal Investigator     Patrick R. Hof Co-Investigator     Alfredo Rodriguez Sr. Software Engineer     Doug Ehlenberger Software Engineer', NULL, 'http://research.mssm.edu/cnic/tools-ns.html', NULL),
('2654', NULL, '426', NULL, 'Measuring image resolution in optical nanoscopy.', NULL, NULL, NULL, NULL, NULL),
('2655', NULL, '426', NULL, 'Fourier shell correlation threshold criteria.', NULL, NULL, NULL, NULL, NULL),
('2656', NULL, '85', NULL, 'MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ.', NULL, NULL, NULL, NULL, NULL),
('2657', '2657', '93', NULL, 'KNOSSOS - 3D image visualization and annotation tool', 'It is a tool to visualize and annotate volume image data of electron microscopy. Users can annotate objects (e.g. neurons) and skeleton structures. It provides the ability to overlaying the image data with user annotations, representing the spatial structure and the connectivity of labeled objects, and displaying a three dimensional model of it. \r\nIt can be extended by plugins written in python.\r\nA similar, web-based implementation is being developed at webknossos.info.\r\nExample datasets are also available.', 'Max Planck Institute for Medical Research in Heidelberg, Germany', NULL, 'http://knossostool.org/', NULL),
('2658', NULL, '93', NULL, 'neuTube 1.0: A New Design for Efficient Neuron Reconstruction Software Based on the SWC Format 1,2,3', NULL, NULL, NULL, NULL, NULL),
('2661', NULL, '93', NULL, 'SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data', NULL, NULL, NULL, NULL, NULL),
('2662', NULL, '426', NULL, 'Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ.', NULL, NULL, NULL, NULL, NULL),
('2660', '2660', '426', NULL, 'fairSIM', 'An easy-to-use plugin that provides SR-SIM reconstructions for a wide range of SR-SIM platforms directly within ImageJ. For research groups developing their own implementations of super-resolution structured illumination microscopy, fairSIM takes away the hurdle of generating yet another implementation of the reconstruction algorithm. For users of commercial microscopes, it offers an additional, in-depth analysis option for their data independent of specific operating systems. As a modular, open-source solution, fairSIM can easily be adapted, automated and extended as the field of SR-SIM progresses.\r\n\r\n<bib>2662</bib>', 'Marcel Müller', NULL, 'https://fairsim.github.io/', NULL),
('2643', '2643', '587', NULL, 'SR-Tesseler', 'Localization-based super-resolution techniques open the door to unprecedented analysis of molecular organization. This task often involves complex image processing adapted to the specific topology and quality of the image to be analyzed.\n\nSR-Tesseler <bib>2661</bib> is an open-source segmentation software using Voronoï tessellation constructed from the coordinates of localized molecules. It allows precise, robust and automatic quantification of protein organization at different scales, from the cellular level down to clusters of a few fluorescent markers.\n\nSR-Tesseler is insensitive to cell shape, molecular organization, background and noise, allowing comparing efficiently different biological conditions in a non-biased manner, and perform quantifications on various proteins and cell types.\n\nSR-Tesseler software comes with a very simple and intuitive graphical user interface, providing direct visual feedback of the results and is freely available under GPLv3 license.', 'Florian Levet, Jean-Baptiste Sibarita', NULL, 'http://www.iins.u-bordeaux.fr/team-sibarita-SR-Tesseler', NULL),
('2665', NULL, '426', NULL, 'SIMToolbox: a MATLAB toolbox for structured illumination fluorescence microscopy.', NULL, NULL, NULL, NULL, NULL),
('2664', NULL, '85', NULL, 'classification of hemp fibers based on morphological features', 'In this workflow, you can use MorphoLibJ to generate accurate morphometric measurements  \r\n\r\n\r\n- First the fibers are segmented by mathematical morphology: for example by using MorphoLibJ:   \r\n\r\n\r\n   Create a marker image by creating a rough mask with extended regional maxima (similar to Find Max), such that you have one max per fiber   \r\n   Use the marker controlled watershed (in MorpholLibJ/ Segentation/ marker controlled watershed) : indicate the orginal grayscale image as the input, Marker will e your maxima image, select None for mask   \r\nit will create a label mask of your fibers     \r\n\r\n\r\n- In MorphoLibJ /A analyze/ select Region Morphometry: this will compute different shape factors which are more robust than the ones implemented by default in ImageJ    \r\n\r\n  \r\n- Export the result table created to a csv file       \r\n\r\n\r\n- Then for example in Matlab or R, you can apply a PCA analysis (Principal component analysis) followed by a k-means with the number of class (clusters) (different fibers type) you want to separate.    \r\n\r\n\r\n- You can then add this class as a new feature to your csv file.   \r\n\r\n         \r\n- From this you can sort your labelled fibers into these clusters for a visual feedback or further spatial analysis              \r\n\r\n\r\nDEPENDS ON MorphoLibJ\r\n\r\n', 'David Legland', NULL, NULL, NULL),
('2651', NULL, '469', NULL, 'Tutorial about MorphoLibJ: using morphological reconstructions to isolate objects', 'When trying to isolate objects, one strategy might be to use regular morphological operations (opening/closing) to remove small objects that are not of interest.\r\nIn case small objects are made of a large number of pixels, this operation might impair the remaining objects\' contours.\r\nAlternative strategy might be to use morphological reconstruction. In short, seed are placed on the image, on objects, then conditionnal dilation is performed from those seeds.\r\n\r\nHere is how to proceed, using MorphoLibJ:\r\n\r\n* Open an image\r\n* Use the multi-point selection tool and place seeds on objects of interest\r\n* Create a new image of same size, black background\r\n* Transfer the selection to the new image (Edit/Selection/Restore selection)\r\n* Draw (make sure you\'re using white foreground) the multiple point selection\r\n* Launch the  Morphological reconstruction plugin: Plugins/MorphoLibJ/Morphological reconstruction\r\n\r\n', 'Fabrice Cordelières', NULL, NULL, NULL),
('2659', NULL, '85', NULL, 'Grayscale granulometry', 'This imageJ/Fiji plugin provides an analysis of the granulometry inside an image by mathematical morphology. It has sevral option for the structuring element to be used, and the size domain to be tested.\r\nThe output will be both a curve of the remaining content of the image against the growing size of the structuring element, and the corresponding results table that could be then exported. \r\nIt can deal with grayscale images directly, no need to segment the image first.\r\nThis plugin can then be used to compare different texture based on some statistical analysis of the produced curve (for exemple comparison of the geometrical means to discriminate 2 textures).\r\nIt is macro recordable as well.\r\n\r\nProgramming Language:  java\r\nProcesses: successive  erosion, dilation, closing or opening -> ANALYSIS\r\n\r\nUser skills: Life Scientist, developers, analysts\r\n\r\n', 'David Legland', NULL, 'https://github.com/dlegland/ijGranulometry', NULL),
('2653', NULL, '592', NULL, 'MorphoLibJ', 'A plugin for ImageJ with funcionalities for image processing such as filtering, reconstructing, segmenting, etc. based on Mathematical morphology.\r\n\r\nPlugin/API\r\n\r\nProgramming language: JAVA\r\n\r\nProcesses:\r\n\r\n-Morphlogical operations\r\n\r\n* Dilation\r\n* Erosion\r\n* Opening\r\n* Closing\r\n* Top hat (white and black)\r\n* Gradient\r\n* Laplacian\r\n\r\n-Morphological reconstruction\r\n\r\n-Maxima/Minima\r\n\r\n-Extended Maxima/Minima\r\n\r\n-Watershed (classic or controlled)\r\n\r\n-Image overlay\r\n\r\n-Image labelling \r\n\r\n-Geodesic diameter \r\n\r\n-Region Adjacency Graph\r\n\r\n-Granulonetry curves\r\n\r\nUser skill : Life scientist, developers, analysts\r\n\r\n\r\n <bib>2656</bib>', 'David Legland, Ignacio Arganda-Carreras', NULL, 'http://imagej.net/MorphoLibJ', NULL),
('2650', '2650', '93', NULL, 'neuTube', 'neuTube <bib>2658</bib> is open source software for reconstructing neurons from fluorescence microscope images. It has a nice interactive system. It has 3D viewer, which can be clicked in 3D and perform neuron tracing semi-automatically. It can automatic recognize branching points as junctions. Traced neurons can be exported to swc format, which could be imported by various software packages.\r\n\r\n', 'L. Feng, T. Zhao, and J. Kim', NULL, 'http://www.neutracing.com/', NULL),
('2652', NULL, '426', NULL, 'Fourier Ring Correlation', 'Calculate the Fourier ring correlation (FRC). The  FRC can be used as a resolution criterion for super resolution microscopy.\r\nThe Plugin can display a plot of the FRC curve, along with the LOESS smoothed version of the curve. Finally it displays the threshold method used and the intersection of the FRC with the threshold, providing the FIRE number. It can be used on two open images or on pairs of images in batch mode.\r\n\r\n<bib>2654</bib>\r\n<bib>2655</bib>', 'Olivier Burri, Alex Herbert ', NULL, 'http://imagej.net/Fourier_Ring_Correlation_Plugin', NULL),
('2663', '2663', '426', NULL, 'SIMToolbox', 'SIMToolbox: a MATLAB toolbox for structured illumination microscopy\r\n\r\nSIMToolbox is an open-source, modular set of functions for MATLAB designed for processing data acquired by structured illumination microscopy. Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination.\r\n\r\n<bib>2665</bib>', 'Pavel K?ížek, Tomáš Lukeš, Martin Ovesný, Karel Fliegel, and Guy M. Hagen', NULL, 'http://mmtg.fel.cvut.cz/simtoolbox/', NULL),
('2666', NULL, '85', NULL, 'Tomato', 'Tomato confocal slice', NULL, NULL, NULL, NULL),
('2622', NULL, '85', NULL, 'alkaline comet assay image', 'from [bib]2621[/bib] L6 cells were combined with molten LM Agarose (Trevigen) (at 37 °C) at a ratio of 1:10 (v/v), and immediately spread onto CometSlide (Trevigen). After 10 min of gelling at 4 °C, the cells were lysed with lysis solution (Trevigen, USA) at 4 °C for 1 h. Upon three washes in distilled water for 5 min each, alkaline comet assay was performed on the slides plated with L6 cells. In case of alkaline comet assay, the slides were placed in a horizontal electrophoresis tank containing electrophoresis solution (0.3 M NaOH (Merck, Germany), and 1 mM EDTA (Bio Rad, USA), pH>13) and allowed to denature for 30 min. Electrophoresis was run for 25 min (1 V/cm).The slides were rinsed with 0.4 M Tris, pH 7.5 neutralization buffer for 3× 5 min. The slides were then washed in distilled water and immersed in 70% ethanol for 5 min and dried at 40 °C for 10–15 min, and then stained with SYBR green 1 dye (Molecular probes, Invitrogen, USA). Comet images were captured using a Zeiss Axioplan fluorescent microscope. ', NULL, NULL, NULL, NULL),
('2669', '2669', '426', NULL, 'MicrobeJ', 'This is an ImageJ plugin to analyze bacterial cells.  It provides a user-friendly interface and a powerful suite of detection, analysis and data presentation tools. It works with individual phase or fluorescence images as well as stacks, hyperstacks, and folders of any of these types. Even large image sets are analyzed rapidly generating raw tabular data that can either be saved or copied as is, or have additional statistical analysis performed and graphically represented directly from within MicrobeJ, making it an all-in-one image analysis solution.', 'Adrien Ducret, Yves Brun, Ellen Quardokus', NULL, 'http://www.indiana.edu/~microbej/index.html', NULL),
('2670', '2670', '426', NULL, 'ThunderSTORM', 'ThunderSTORM is an open-source, interactive, and modular plug-in for ImageJ designed for automated processing, analysis, and visualization of data acquired by single molecule localization microscopy methods such as PALM and STORM. Our philosophy in developing ThunderSTORM has been to offer an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data.', 'M. Ovesný, P. K?ížek, J. Borkovec, Z. Švindrych, G. M. Hagen', NULL, 'http://zitmen.github.io/thunderstorm/', NULL),
('2672', '2672', '426', NULL, 'Vision4D', 'arivis Vision4D is a modular software for working with multi-channel 2D, 3D and 4D images of almost unlimited size independent of available RAM.\r\n\r\nMany imaging systems, such as high speed confocal, Light Sheet/ SPIM and 2 Photon systems, can produce a huge amount of multi-channel data, which arivis Vision4D handles without constraints.\r\nTerabyte ready\r\narivis Vision4D main functionality:\r\n\r\n    Easy import of most image formats from microsopes as well as biological formats\r\n    High performance interactive 3D / 4D rendering on standard PCs and laptops with 3D Graphics Support\r\n    Intuitive tools for stitching and alignment to create large multi-dimensional image stacks\r\n    Immediate 2D, 3D and 4D visualization, annotation and analysis regardless of image size\r\n    Creation, import, and export of 4D Iso-surfaces\r\n    Powerful Analysis Pipeline for 3D /4D image analysis (cell segmentation, tracking, annotation, quantitative measurement and statistics, etc)\r\n    Semi-automatic/manual segmentation and tracking with interactive Track Editor\r\n    Easy design and export of 3D / 4D High Resolution Movies\r\n    Seamless integration of custom workflows via Matlab API and Python scripting\r\n    Data sharing for collaboration\r\n    A user friendly software, easy to learn and use for any life scientist\r\n', NULL, NULL, 'https://www.arivis.com/en/imaging-science/arivis-vision4d', NULL),
('2673', '2673', '592', NULL, 'SuReSim', 'SuReSim (Super Resolution Simulation) is an open-source simulation software for Single Molecule Localization Microscopy (SMLM). The workflow of the SuReSim algorithm starts from a ground truth structure and lets the user choose to either directly simulate 3D localizations or to create simulated *.tiff-stacks that the user can analyze with any given SMLM reconstruction software.\r\n\r\nA 3D structure of any geometry, either taken from electron microscopy, designed de-novo from assumptions or known structural facts, is fluorophore-labeled in silico. A defined set of parameters is used to calculate and visualize the 3D localizations of the corresponding labels.\r\n\r\nThe software package is accompanied with a library of model structures that can be imported and simulated.\r\n\r\nUsers manual with tutorial provided.\r\n\r\n', 'Varun Venkataramani, Frank Herrmannsdörfer, Mike Heilemann and Thomas Kuner', NULL, 'http://www.ana.uni-heidelberg.de/?id=198', NULL),
('2677', NULL, '592', NULL, 'Mosaic Suite', NULL, NULL, NULL, NULL, NULL),
('2683', NULL, '93', NULL, 'A Parallel Distributed-Memory Particle Method Enables Acquisition-Rate Segmentation of Large Fluorescence Microscopy Images', NULL, NULL, NULL, NULL, NULL),
('2687', NULL, '587', NULL, 'Clus-DoC: A combined cluster detection and colocalization analysis for single-molecule localization microscopy data.', NULL, NULL, NULL, NULL, NULL),
('2688', '2688', '587', NULL, 'Clus-Doc', 'Clus-Doc is a software  that quantifies both the spatial distribution of a protein as well as its colocalization status. It  may be used to quantify signaling activity and protein colocalization at the level of individual proteins.', 'Sophie V. Pageon, Philip R. Nicovich, Mahdie Mollazade, Thibault Tabarin, Katharina Gaus', NULL, 'https://github.com/PRNicovich/ClusDoC', NULL),
('2689', NULL, '587', NULL, 'Real-time analysis and visualization for single-molecule based super-resolution microscopy.', NULL, NULL, NULL, NULL, NULL),
('2691', NULL, '587', NULL, 'A software framework for the analysis of complex microscopy image data.', NULL, NULL, NULL, NULL, NULL),
('2694', '2694', '587', NULL, 'MIATool', 'The Microscopy Image Analysis Tool (MIATool) is a software application designed for the viewing and processing of N-dimensional array of images. At its core is an image viewer which allows the traversal of an N-dimensional array of images. Besides the standard display as pixels of varying intensity values, options are available to view the images as mesh or contour plots. The current version of MIATool supports four different image editing tools which can be used to process the images displayed in the viewer. The intensity adjustment tool provides different ways to modify the pixel intensity values, and the crop tool allows trimming of the images to retain only the portion that is of interest. The two remaining tools - the segmentation tool and the label tool - can be used for manual image segmentation and image labeling.', NULL, NULL, 'http://www.wardoberlab.com/software/miatool/', NULL),
('2695', NULL, '587', NULL, 'Fast Optimized Cluster Algorithm for Localizations (FOCAL): a spatial cluster analysis for super-resolved microscopy.', NULL, NULL, NULL, NULL, NULL),
('2696', '2696', '587', NULL, 'FOCAL', 'FOCAL (Fast Optimized Cluster Algorithm for Localizations) is a rapid density based algorithm for detecting clusters in localization microscopy datasets. ', 'A. Mazouchi, JN. Milstein ', NULL, 'http://www.mybiosoftware.com/focal-cluster-analysis-for-super-resolved-microscopy.html', NULL),
('2697', NULL, '587', NULL, 'A guided tour into subcellular colocalization analysis in light microscopy.', NULL, NULL, NULL, NULL, NULL),
('2701', NULL, '426', NULL, 'Intravital placenta imaging reveals microcirculatory dynamics impact on sequestration and phagocytosis of Plasmodium-infected erythrocytes.', NULL, NULL, NULL, NULL, NULL),
('2700', NULL, '426', NULL, 'StabiTissue', '- 2D Stabilization in each slice of the stacks in time.\r\n- 3D Stabilization intravital imaging of all the stacks (including the dimension Z)\r\n- create the videos and the stabilized images in a new folder\r\n\r\n<bib>2701</bib>', 'Iván Gómez Conde, David Olivieri, Carlos Tadokoro', NULL, 'https://sourceforge.net/projects/stabitissue/', NULL),
('2702', NULL, '587', NULL, 'SynPAnal: software for rapid quantification of the density and intensity of protein puncta from fluorescence microscopy images of neurons.', NULL, NULL, NULL, NULL, NULL),
('2703', '2703', '587', NULL, 'SynPAnal', 'This software is designed for the rapid semi-automatic detection and quantification of synaptic protein puncta from 2D immunofluorescence images generated by confocal laser scanning microscopy.', 'Eric Danielson, Sang H. Lee', NULL, 'https://sourceforge.net/projects/synpanal/', NULL),
('2704', NULL, '587', NULL, 'Automatic Bayesian single molecule identification for localization microscopy.', NULL, NULL, NULL, NULL, NULL),
('2705', '2705', '587', NULL, 'Auto-Bayes', 'Auto-Bayes is a software package based on Bayesian statistics and requires no user dependent parameters for molecule detection and image reconstruction for Single-Molecule Localization Microscopy (SMLM), including photoactivated localization microscope (PALM), stochastic optical reconstruction microscope (STORM), and direct stochastic optical reconstruction microscopy (dSTORM), etc.', 'Yunqing Tang, Johnny Hendriks, Thomas Gensch, Luru Dai & Junbai Li', NULL, 'http://english.nanoctr.cas.cn/dai/software/201505/t20150504_146858.html', NULL),
('1944', NULL, '0', NULL, 'HK-Means', 'This segmentation method performs a N-class thresholding based on a K-Means classification of the image histogram, then extracts objects in a bottom-up manner using user-defined minimum and maximum object sizes. Very useful to detect clustered objects in fluorescence microscopy.', 'Alexandre Dufour', NULL, 'http://icy.bioimageanalysis.org/plugin/HK-Means', NULL),
('2708', NULL, '93', NULL, 'ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains', NULL, NULL, NULL, NULL, NULL),
('2709', '2709', '93', NULL, 'ViBE-Z', 'The Virtual Brain Explorer (ViBE-Z) is a software <bib>2708</bib> that automatically maps gene expression data with cellular resolution to a 3D standard larval zebrafish (Danio rerio) brain. It automatically detects 14 predefined anatomical landmarks for aligning data. It also offers a database and atlas.\r\nThe ViBE-Z database, atlas and software are provided via a web interface. A data preparation step is needed in order to provide the right input data and format.', 'Olaf Ronneberger', NULL, 'http://vibez.informatik.uni-freiburg.de/', NULL),
('2671', NULL, '93', NULL, '3D segmentation (reconstruction) and modeling using Free-D', 'Free-D (http://free-d.versailles.inra.fr/) is a 3D reconstruction and modeling software. It is multiplatform, free (but not open source) tool for academic research and teaching.\r\n\r\nHere is how to proceed, using Free-D:\r\n\r\n\r\n1. Segmentation:   \r\n    * load (a collection of) individual 3d stacks   \r\n    * (optional for serial sections) perform a 2D registration to align image slices   \r\n    * segment/reconstruct 3D contours using snakes  \r\n    * segment 3D spots   \r\n2. Construct average cell:   \r\n    * normalize the contours to compute a average cell, by registering/warping 3D contours/surfaces   \r\n3. Quantification:    \r\n    * project each individual cell to the average one   \r\n    * build density maps to analyze (cartography)   \r\n\r\n\r\n\r\nA few notes for current software version (till 10/2016):   \r\n* input file format: tiff (not able to import bioformats)   \r\n* currently results are saved in customized format, but there is an exportor to convert this format into fiji readable one   \r\n* import already generated contours is on the software\'s TODO list   ', 'Chong Zhang', NULL, NULL, NULL),
('2698', NULL, '587', NULL, 'JACoP', 'This ImageJ plug-in is a compilation of co-localization tools. It allows:\r\n\r\n-Calculating a set of commonly used co-localization indicators:\r\n\r\nPearson\'s coefficient\r\nOverlap coefficient\r\nk1 & k2 coefficients\r\nManders\' coefficient\r\nGenerating commonly used visualizations:\r\n\r\n-Cytofluorogram\r\n\r\nHaving access to more recently published methods:\r\n\r\n-Costes\' automatic threshold\r\n\r\nLi\'s ICA\r\nCostes\' randomization\r\nObjects based methods (2 methods: distances between centres and centre-particle coincidence)', 'Fabrice P. Cordelières, Susanne Bolte', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:jacop_2.0:just_another_colocalization_plugin:start', NULL),
('2690', NULL, '587', NULL, 'WaveTracer', 'WaveTracer is a plugin for Metamorph. It represents a functional method for real-time reconstruction with automatic feedback control, without compromising the localization accuracy. It relies on a wavelet segmentation algorithm, together with a mix of CPU/GPU implementation.', 'Adel Kechkar, Deepak Nair, Mike Heilemann, Daniel Choquet, Jean-Baptiste Sibarita', NULL, 'http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0062918', NULL),
('2686', NULL, '587', NULL, 'Distance Analysis (DiAna)', 'This plugin allows :\r\n\r\nCalculating co-localization between objects in 3D\r\nMeasuring 3D distances between nearest object, co-localized or not\r\nGetting some 3D measurements about each objects\r\nThe plugin can be used with labelled images, but it also integrates tools for the segmentation of the objects.\r\n\r\nProgramming language: JAVA\r\n\r\nProcesses:\r\nDenoise filter\r\n\r\nSegmentation of the objects\r\n\r\nObject based co-localization and distance analysis\r\n\r\nCounting and measurements on objects', 'Jean-Francois Gilles, Thomas Boudier', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:distance_analysis_diana_2d_3d_:start', NULL),
('2685', NULL, '93', NULL, 'DeconvolutionLab2', NULL, NULL, NULL, 'http://bigwww.epfl.ch/deconvolution/deconvolutionlab2/', NULL),
('2684', NULL, '93', NULL, '2D-3D distributed parallel region competition segmentation', 'This is the source code and data page of a distributed parallel algorithm <bib>2683</bib> for segmentation of large fluorescence microscopy images. ', 'MOSAIC Group, Dresden', NULL, 'https://github.com/yafshar/PPM_RC', NULL),
('2682', NULL, '592', NULL, 'MosaicSuit', 'Image-processing algorithms developed at the MOSAIC Group for fluorescence microscopy. Tools included: \r\n* 2D/3D single-particle tracking tool which can be used to track bright spots in 2D/3D movies over time.\r\n* Optimal filament segmentation of 2D images with previously unknown number of filaments. It can produce sub-pixel accuracy results and handle different types of image data from different microscopy modalities. \r\n* Curvature filters for image filtering, denoising, and restoration. Currently implemented Gauss curvature, Mean curvature, and Total Variation (TV) filters. The only parameters is the number of iterations, i.e., how many passes of the filter should be applied to the image. Else the filters are parameter free. A C++ implementation of these filters is also available. \r\n* Image naturalization for image enhancement based on gradient statistics of natural-scence images. The algorithm is completely parameter free. In fluorescence microscopy, image naturalization can be used for blind deconvolution, dehazing (removing scatter light), denoising, or contract enhancement. \r\n*Tool for automatically send and distribute jobs on clusters and get back the results. \r\n* Multi-region image segmentation of 2D and 3D images without needing to know the number of regions beforehand. The method can handle objects with internal intensity gradients (i.e., shaded objects), but is limited to pixel-level accuracy and only delivers locally optimal results that depend on the initialization.\r\n* Squassh for globally optimal segmentation of piecewise constant regions in 2D and 3D images and for object-based co-localization analysis. Globally optimal segmentation is very fast and independent of initialization, but it is currently limited to objects with homogeneous (i.e., constant) internal intensity. The method can also produce sub-pixel accurate results.\r\n* Tool for inferring spatial interactions between patterns of objects in images or between coordinates read from a file.\r\n* Tool for robust, histogram-based background subtraction well suited to correct for inhomogeneous illumination artifacts.\r\n* This plugin can be used to estimate the Point-Spread Function of the microscopy out of 2D fluorescence images.\r\n* Measurement of the 3D Point-Spread Function of a confocal microscope from an image stack.\r\n* It can add synthetic Poisson-distributed noise to an image in order to simulate shot noise of various signal-to-noise ratios. It can be used to generate benchmark images in order to assess the accuracy and robustness of image processing algorithms as a function of the noise level present in images. \r\n* This plugin convolves an image with a Bessel function in order to simulate imaging with a microscope. The Bessel function is a model for a generic Point-Spread Function, depending on the wavelength and NA. This plugin can be used to generate synthetic images that mimic microscope output in order to benchmark the accuracy and robustness of image processing algorithms.\r\n* Small utility to detect bright spots in images and estimate their center. This can be handy in an image-processing pipeline or macro where only detection is needed. It can also be combined with Squash for segmentation patch positioning, and it handles time-series and video data.\r\n*  An utility to create manual segmentations to be used as ground truth to test and benchmark automatic segmentation algorithms.\r\n* A tool for replacing one color in an image with another color. \r\n', 'Ivo F. Sbalzarini MOSAIC GROUP', NULL, 'http://mosaic.mpi-cbg.de/?q=downloads/imageJ', NULL),
('2676', NULL, '85', NULL, 'Morpholeaf', ' The MorphoLeaf application allows you to extract the contour of multiple leaf images and identify their biologically-relevant landmarks.\r\n\r\nThese landmarks are then used to quantify morphological parameters of individual leaves and to reconstruct average leaf shapes.\r\n\r\nMorphoLeaf is developed by the Modeling and Digital Imaging and the Transcription Factors and Architecture teams of the Institut Jean-Pierre Bourgin, INRA Versailles, France, and the Biophyscis and Development group at RDP, Lyon. ', 'Eric Biot (eric biot AT versailles inra fr) ', NULL, 'http://morpholeaf.versailles.inra.fr/', NULL),
('2674', NULL, '592', NULL, ' ec-CLEM', 'This plugin allows to compute a similarity (translation/rotation/scaling and flipping) transform from pair of points. It is updating the transformed image interactively such that the user get immediate feedback. The transformation is saved and can be applied to any other stack/image. Non rigid deformation can also be applied in 2D or 3D.\r\n\r\n3D/3D,2D/3D or 3D /2D can be handled . \r\n\r\n3D ROI are enabled, and can be checked with the 3D vtk view (size of ROI can be changed using the ROI stroke width). \r\n\r\nSome prelalignment by rotating in 3D the volume is possible. \r\n\r\nTransformations can be applied directly or combined through Block Protocols (search for apply transformation).\r\n\r\nIt\'s also provide information about the predicted Error (based on statistical prediction as described by Fitzpatrick et al), either as a full color mapping, either on each points used as landmarks, and error on the discrepancy in position between points.\r\n\r\nThere are video tutorials available in the web.\r\n\r\nPublication: "Open Source Image registration plugin for correlative light to electron microscopy: e¢-CLEM easy Cell Correlative Light to Electron Microscopy". Xavier Heiligenstein*, Perrine Paul-Gilloteaux*, Martin Belle, Marie-Charlotte Domart, Banafshe Larijani, Lucy Collinson, Graça Raposo Jean Salamero. In preparation, 2016.', ' Perrine Paul-Gilloteaux', NULL, 'http://icy.bioimageanalysis.org/plugin/ec-CLEM', NULL),
('2699', NULL, '93', NULL, 'PSF Lab', 'PSF Lab is a software program that calculates the illumination point spread function of a confocal microscope under various imaging conditions. It is available in 32-bit and 64-bit for Windows and in 64-bit for Mac.', 'he One Molecule Group', NULL, 'http://onemolecule.chem.uwm.edu/software', NULL),
('2667', NULL, '426', NULL, 'MultiStackReg', 'This plugin aligns the slices of a stack just like the stackreg plugin on which it is built. It allows to save the transformations and to apply them to another stack. It furthermore allows to register two stacks. ', 'Brad Busse', NULL, 'http://bradbusse.net/downloads.html', NULL),
('2675', '2675', '85', NULL, 'FreeD', ' Free-D is a three-dimensional (3D) reconstruction and modeling software. It allows to generate, process and analyze 3D point and surface models from stacks of 2D images.\r\n\r\nFree-D is an integrated software, offering in a single graphical user interface all the functionalities required for 3D modeling. It runs on Linux, Windows, and MacOS.\r\n\r\nFree-D is developed by the Modeling and Digital Imaging team of the Institut Jean-Pierre Bourgin, INRA Versailles, France. ', 'Andrey P & Maurin Y', NULL, 'http://free-d.versailles.inra.fr/', NULL),
('2679', '2679', '581', NULL, 'Balanced SOFI', 'Super-resolution optical fluctuation imaging (SOFI) achieves 3D super-resolution by computing temporal cumulants or spatio-temporal cross-cumulants of stochastically blinking fluorophores [1,2]. In contrast to localization microscopy, SOFI is compatible with weakly emitting fluorophores and a wider range of blinking conditions [3]. Balanced SOFI analyses several cumulant orders for extracting molecular parameter maps, such as the bright and dark state lifetimes, the concentration and the brightness distributions of fluorophores within biological samples [a]. In combination with a deconvolution of the cumulant images, the estimated parameter maps proved useful to balance the image contrast and to linearize the brightness and blinking response. Thereby, the image quality and the resolution were improved significantly.', 'Marcel Leutenegger', NULL, 'https://documents.epfl.ch/users/l/le/leuteneg/www/BalancedSOFI/index.html', NULL),
('2710', NULL, '11', NULL, 'Analyze Particles', 'An object detection function in ImageJ. [Analyze > Analyze Particles...]. \r\n\r\nIt could simply be used for counting number of cells, but could also do more complex stuffs. \r\n\r\n## Jython Snippet\r\n\r\nHere is a snippet of how to use Particle Analysis in Jython script. \r\n\r\n<http://cmci.embl.de/documents/120206pyip_cooking/python_imagej_cookbook#particle_analysis>\r\n\r\n', NULL, NULL, 'http://rsbweb.nih.gov/ij/docs/guide/146-30.html#toc-Subsection-30.2', NULL),
('2714', NULL, '11', NULL, 'Infection Counter: Automated Quantification of in Vitro Virus Replication by Fluorescence Microscopy.', NULL, NULL, NULL, NULL, NULL),
('2715', NULL, '11', NULL, 'Pink / Digital Connectivity', NULL, NULL, NULL, 'https://pinkhq.com', NULL),
('2717', NULL, '11', NULL, 'NeurphologyJ', 'ImageJ macro for the morphometry of neurites. \r\n\r\n> NeurphologyJ; it is capable of automatically quantifying neuronal morphology such as soma number and size, neurite length, neurite ending points and attachment points. NeurphologyJ is implemented as a plugin to ImageJ, an open-source Java-based image-processing and analysis platform.\r\n\r\n### Reference\r\n\r\n<http://www.biomedcentral.com/1471-2105/12/230>', 'Eric Hwang', NULL, NULL, NULL),
('2720', '2720', '11', NULL, 'XuvTools', 'XuvTools (pronounced “ex-you-vee-tools”) is a fully automated 3D stitching software for biomedical image data, typically confocal microscopy images. XuvTools runs on Microsoft Windows XP and Vista, Linux and Apple Mac computers. It supports 32 and 64bit operating systems (with 64bit highly preferred). XuvTools is free and open source software (see Licensing), so you can start using it immediately. Go to Downloads and give it a try.\r\n\r\nThe goal of XuvTools is to provide tools, that combine multiple microscopic recordings to obtain a larger field of view (“stitching”) and a higher dynamic range (“HDR” recombination), or better resolution (multi view reconstruction), and to make these tools publicly available.', 'Mario Emmenlauer, Aaron Ponti, Olaf Ronneberger', NULL, 'http://www.xuvtools.org', NULL),
('2718', '2718', '159', NULL, 'Huygens Remote Manager', 'The Huygens Remote Manager is an open-source, efficient, multi-user web-based interface to the Huygens software by Scientific Volume Imaging for parallel batch deconvolutions.', 'Volker Baecker, Aaron Ponti, Asheesh Gulati, Alessandra Griffa, José Viña, Daniel Sevilla, Niko Ehrenfeuchter, Torsten Stöter, Olivier Burri', NULL, 'http://www.huygens-rm.org/', NULL),
('2716', NULL, '85', NULL, 'Microscope Image Correlation Spectroscopy MICS', 'Fluorescence spectroscopy by image correlation is a technique that allows analysing and characterizing the different molecular dynamics from a sequence of fluorescence images.\r\n\r\nMany image correlation techniques have been developed for different applications but in particular to study the mechanisms of cell adhesion during migration. These techniques can be used with most imaging modalities: e.g. fluorescence widefield, confocal microscopy, TIRFM. They allow to obtain information such as the density in molecules, diffusion coefficients, the presence of several populations, or the direction and speed of a movement corresponding to active transport when spatial and temporal correlations are taken into account (STICS: Spatio-Temporal Image Correlation Spectroscopy). \r\nPlease see <bib>2580</bib>  for a review and the description of the methods.\r\n\r\nThis plugin is based on ICS_tools plugin by Fitz Elliott, available [here](http://www.cellmigration.org/resource/imaging/imaging_resources.shtml "cell migration website").\r\n\r\nSome bugs have been removed, ROI does not need to be squared, fitting is weighted in order to give more weight to the smaller lags (temporal or spatial)\r\nExemple of use on sample data [fluorescent beads](http://biii.info/node/2577 "Beads")\r\n- Select an ROI, start by ICS to get the right PSF size\r\n- Then run TICS and select diffusion, or diffusion plus flow model. Remove the first line (autocorrelation) which corresponds to the noise autocorrelation before fitting.\r\n\r\n\r\n\r\n', 'Chen Chen, François Waharte,  Perrine Paul-Gilloteaux', NULL, 'http://imagejdocu.tudor.lu/doku.php?id=plugin:analysis:microscope_image_correlation_spectroscopy:start&s[]=spectroscopy', NULL),
('2719', '2719', '159', NULL, 'IceImarisConnector', 'IceImarisConnector is a simple commodity class that eases communication between Bitplane Imaris and MATLAB or python using the Imaris XT interface.', 'Aaron Ponti', NULL, 'http://www.scs2.net/next/index.php?id=110', NULL),
('2721', '2721', '93', NULL, 'TurtleSeg: 3D Image Segmentation Software', 'TurtleSeg is an interactive 3D image segmentation tool. TurtleSeg has an automated system, Spotlight, for automatically directing the user towards the next steps. Typically, a user loads a 3D image and then manually contour a sparse number of slices, the full 3D segmentation can then be built automatically.', NULL, NULL, 'http://www.turtleseg.org/', NULL),
('2712', NULL, '11', NULL, 'Voronoi ImageJ', 'A resident function in ImageJ, located in the menu as [Process > Binary > Voronoi]. \r\n\r\n\r\nQuote from the ImageJ reference page: Splits the image by lines of points having equal distance to the borders of the two nearest particles. Thus, the Voronoi cell of each particle includes all points that are nearer to this particle than any other particle. When particles are single points, this process is a Voronoi tessellation (also known as Dirichlet tessellation).  \r\nThe output type (Overwrite, 8-bit, 16-bit or 32-bit) of this command can be set in the Process?Binary?Options…? dialog box. In the output, the value inside the Voronoi cells is zero; the pixel values of the dividing lines between the cells are equal to the distance between the two nearest particles. This is similar to a medial axis transform of the background, but there are no lines in inner holes of particles.', 'Wayne Rasband, Michael Schmid', NULL, 'https://github.com/imagej/imagej1/blob/master/ij/plugin/filter/EDM.java', NULL),
('2711', NULL, '11', NULL, 'Find Maxima', NULL, 'Michael Schmid', NULL, 'http://rsbweb.nih.gov/ij/docs/guide/146-29.html#toc-Subsection-29.4', NULL),
('2713', NULL, '11', NULL, 'InfectionCounter', 'Estimate the frequency of hepatitis C virus infected cells based on the intensity of viral antigen associated immunofluorescence. \r\n\r\n\r\nReference <bib>2651</bib>', 'Joe Grove', NULL, NULL, NULL);
INSERT INTO `entity` (`id_entity`, `id_user_skill`, `id_curator`, `id_paper`, `Title`, `Body`, `Authors`, `Training material`, `LocationPath`, `ImagePath`) VALUES
('2201', NULL, '0', NULL, 'Squassh', '‘’’Squassh’’’ is a tool for 2D and 3D segmentation and quantification of subcellular shapes in fluorescence microscopy images. It provides globally optimal detection and segmentation of objects with constant internal intensity distribution, followed by object-based colocalization analysis. The segmentation computed by Region Competition can optionally correct for the PSF of the microscope, hence providing optimally deconvolved segmentations.\r\n\r\nPart of the mosaic suite', 'MOSAIC Group, Center for Systems Biology Dresden (CSBD), Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG), Dresden, Germany. ', NULL, 'http://imagej.net/Squassh', NULL),
('2723', NULL, '426', NULL, 'jicbioimage: a tool for automated and reproducible bioimage analysis', NULL, NULL, NULL, NULL, NULL),
('2722', '2722', '426', NULL, 'jicbioimage', 'The jicbioimage [bib]2723[/bib] Python package makes it easy to explore microscopy data in a programmatic fashion. Exploring images via coding means that the exploratory work becomes recorded and reproducible. Furthermore, it makes it easier to convert the exploratory work into (semi) automated analysis work flows.\r\n\r\nFeatures\r\n    Built in functionality for working with microscopy data\r\n    Automatic generation of audit trails\r\n    IPython integration\r\n    Cross-platform: Linux, Mac and Windows are all supported\r\n    Works with Python 2.7, 3.3 and 3.4\r\n\r\n', ' Tjelvar S. G. Olsson?, Matthew Hartley ', NULL, 'http://jicbioimage.readthedocs.io/en/latest/', NULL),
('2724', NULL, '587', NULL, 'SQL', 'SQL (Structured Query Language) is a special-purpose domain-specific language used in programming and designed for managing data held in a relational database management system (RDBMS), or for stream processing in a relational data stream management system (RDSMS).', NULL, NULL, NULL, NULL),
('2725', NULL, '587', NULL, 'FORTRAN', 'Fortran (formerly FORTRAN, derived from \'Formula Translation\') is a general-purpose, imperative programming language that is especially suited to numeric computation and scientific computing. Originally developed by IBM in the 1950s for scientific and engineering applications, Fortran came to dominate this area of programming early on and has been in continuous use for over half a century in computationally intensive areas such as numerical weather prediction, finite element analysis, computational fluid dynamics, computational physics, crystallography and computational chemistry. It is a popular language for high-performance computing and is used for programs that benchmark and rank the world\'s fastest supercomputers.', NULL, NULL, NULL, NULL),
('2726', NULL, '587', NULL, 'PHP', 'PHP is a server-side scripting language designed primarily for web development but also used as a general-purpose programming language.', NULL, NULL, NULL, NULL);

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-- --------------------------------------------------------

--
-- Structure de la table `keywords`
--

CREATE TABLE `keywords` (
  `id_keyword` decimal(10,0) NOT NULL DEFAULT '0',
  `Name` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `keywords`
--

INSERT INTO `keywords` (`id_keyword`, `Name`) VALUES
('110', '24-bit RGB cameras'),
('134', 'Acclimatization'),
('297', 'Actin Cytoskeleton'),
('220', 'Actinin'),
('158', 'Actins'),
('248', 'adaptive band-pass filtering'),
('240', 'adaptive filters'),
('212', 'Adenocarcinoma'),
('231', 'Adenosine Triphosphatases'),
('15', 'Adenoviridae'),
('198', 'Adult'),
('199', 'African Continental Ancestry Group'),
('8', 'Algorithms'),
('200', 'Anemia, Sickle Cell'),
('78', 'Aniline Compounds'),
('9', 'Animals'),
('55', 'Antigens, CD29'),
('102', 'Antigens, CD36'),
('280', 'Ants'),
('135', 'Arabidopsis'),
('89', 'Artemia'),
('10', 'Automation'),
('232', 'Bacteria'),
('259', 'Band pass filters'),
('241', 'band-pass filters'),
('90', 'Behavior, Animal'),
('260', 'Bidirectional control'),
('249', 'bidirectional object tracking'),
('250', 'bidirectional particle tracking'),
('113', 'Biological cells'),
('251', 'biological image'),
('107', 'biological tissues'),
('127', 'Biological Transport'),
('261', 'Biology'),
('108', 'biology computing'),
('136', 'Biomass'),
('122', 'Biomedical image processing'),
('109', 'biomedical imaging'),
('123', 'Biomedical microscopy'),
('124', 'Biomedical signal detection'),
('302', 'Blood Flow Velocity'),
('34', 'Breast Neoplasms'),
('270', 'Bronchography'),
('56', 'Cadherins'),
('70', 'Calcium'),
('71', 'Calcium Signaling'),
('137', 'Carbohydrate Metabolism'),
('138', 'Carbon'),
('139', 'Carboxylic Acids'),
('72', 'Cats'),
('233', 'Caulobacter crescentus'),
('57', 'Cell Adhesion'),
('65', 'Cell Compartmentation'),
('192', 'Cell Culture Techniques'),
('58', 'Cell Differentiation'),
('16', 'Cell Line'),
('177', 'Cell Line, Tumor'),
('59', 'Cell Movement'),
('169', 'Cell Nucleus'),
('66', 'Cell Nucleus Structures'),
('103', 'Cell Survival'),
('285', 'Cells'),
('79', 'Cells, Cultured'),
('286', 'Cellulose'),
('287', 'Centromere'),
('301', 'Cercopithecus aethiops'),
('271', 'Cerebral Arteries'),
('91', 'Cestoda'),
('92', 'Cestode Infections'),
('201', 'Child'),
('221', 'CHO Cells'),
('35', 'Chromogenic Compounds'),
('202', 'Chromosome Mapping'),
('234', 'Chromosomes, Bacterial'),
('316', 'Cluster Analysis'),
('27', 'Codes of Ethics'),
('36', 'Color'),
('178', 'Colorectal Neoplasms'),
('37', 'Coloring Agents'),
('1', 'Computational Biology'),
('152', 'Computer Graphics'),
('80', 'Computer Simulation'),
('153', 'Computer Systems'),
('300', 'COS Cells'),
('222', 'Cricetinae'),
('223', 'Cricetulus'),
('295', 'Cryoelectron Microscopy'),
('252', 'curvelet analysis'),
('265', 'Curvelet transform'),
('242', 'curvelet transforms'),
('207', 'Cytological Techniques'),
('28', 'Data Compression'),
('191', 'decay'),
('114', 'Deconvolution'),
('115', 'Detectors'),
('38', 'Diagnostic Imaging'),
('243', 'diagnostic radiography'),
('116', 'Diffraction'),
('253', 'directional band-pass filtering'),
('98', 'DNA, Neoplasm'),
('129', 'Drosophila melanogaster'),
('128', 'Drosophila Proteins'),
('179', 'Drug Discovery'),
('81', 'Electrophysiology'),
('224', 'Embryo, Mammalian'),
('17', 'Endosomes'),
('60', 'Enteric Nervous System'),
('52', 'Equipment Design'),
('154', 'Equipment Failure Analysis'),
('303', 'Erythrocytes'),
('235', 'Escherichia coli'),
('236', 'Escherichia coli Proteins'),
('29', 'Ethics, Research'),
('67', 'Eukaryotic Cells'),
('203', 'European Continental Ancestry Group'),
('213', 'Exocytosis'),
('214', 'Extracellular Matrix'),
('39', 'Female'),
('225', 'Fibroblasts'),
('190', 'fit'),
('187', 'flim'),
('117', 'Fluorescence'),
('254', 'fluorescence image sequence'),
('211', 'Fluorescence microscopy'),
('255', 'fluorescence microscopy image'),
('294', 'Fluorescence Polarization'),
('166', 'Fluorescence Resonance Energy Transfer'),
('244', 'fluorescence spectroscopy'),
('167', 'Fluorescent Dyes'),
('256', 'fluorescent particle'),
('278', 'Focal Adhesions'),
('296', 'Fourier Analysis'),
('40', 'Gene Expression'),
('140', 'Gene Expression Regulation'),
('99', 'Genetic Markers'),
('170', 'Genome'),
('130', 'Glycogen Synthase Kinase 3'),
('226', 'Green Fluorescent Proteins'),
('30', 'Guidelines as Topic'),
('104', 'Haplorhini'),
('208', 'HeLa Cells'),
('298', 'Hep G2 Cells'),
('288', 'Heterochromatin'),
('237', 'High-Throughput Screening Assays'),
('317', 'Hippocampus'),
('41', 'Histocytochemistry'),
('111', 'histology slide normalization'),
('93', 'Host-Parasite Interactions'),
('18', 'Host-Pathogen Interactions'),
('19', 'Humans'),
('186', 'Image Cytometry'),
('155', 'Image Enhancement'),
('185', 'Image Interpretation, Computer-Assisted'),
('197', 'image matching'),
('4', 'Image Processing, Computer-Assisted'),
('118', 'Image retrieval'),
('210', 'Image segmentation'),
('245', 'image sequences'),
('159', 'Imaging, Three-Dimensional'),
('42', 'Immunohistochemistry'),
('100', 'In Situ Hybridization, Fluorescence'),
('119', 'Information retrieval'),
('53', 'Information Storage and Retrieval'),
('22', 'Internet'),
('68', 'Intracellular Membranes'),
('23', 'Intracellular Space'),
('194', 'invariant features'),
('46', 'Kaplan-Meier Estimate'),
('47', 'Ki-67 Antigen'),
('266', 'Kymograph'),
('257', 'kymograph image'),
('189', 'lifetime'),
('299', 'Lighting'),
('165', 'Likelihood Functions'),
('281', 'Locomotion'),
('304', 'Macrophages'),
('272', 'Magnetic Resonance Angiography'),
('273', 'Magnetic Resonance Imaging'),
('305', 'Malaria'),
('61', 'Male'),
('289', 'Mammary Glands, Animal'),
('215', 'Matrix Metalloproteinase 14'),
('120', 'Maximum likelihood estimation'),
('246', 'medical image processing'),
('125', 'Melanoma'),
('62', 'Mice'),
('306', 'Mice, Inbred BALB C'),
('307', 'Mice, Inbred C57BL'),
('180', 'Microarray Analysis'),
('238', 'Microbiological Techniques'),
('308', 'Microcirculation'),
('216', 'Microfilament Proteins'),
('11', 'Microscopy'),
('20', 'Microscopy, Confocal'),
('21', 'Microscopy, Fluorescence'),
('309', 'Microscopy, Fluorescence, Multiphoton'),
('12', 'Microscopy, Interference'),
('13', 'Microscopy, Phase-Contrast'),
('279', 'Microscopy, Video'),
('94', 'Microsporidia'),
('131', 'Microtubules'),
('160', 'Models, Biological'),
('290', 'Models, Statistical'),
('274', 'Models, Theoretical'),
('105', 'Molecular Probe Techniques'),
('291', 'Monte Carlo Method'),
('267', 'Multiple Hypothesis Tracking (MHT)'),
('142', 'Multivariate Analysis'),
('73', 'Muscle Fibers, Skeletal'),
('82', 'Muscle, Skeletal'),
('83', 'Myocardium'),
('74', 'Myocytes, Cardiac'),
('75', 'Myocytes, Smooth Muscle'),
('217', 'Neoplasm Invasiveness'),
('218', 'Neoplasm Metastasis'),
('181', 'Neoplasm Transplantation'),
('182', 'Neoplasms'),
('204', 'Netherlands'),
('63', 'Neural Crest'),
('318', 'Neurons'),
('292', 'Nuclear Proteins'),
('195', 'object recognition'),
('247', 'object tracking'),
('258', 'object trail characteristics'),
('132', 'Oocytes'),
('14', 'Optics and Photonics'),
('69', 'Organelles'),
('282', 'Oryzias'),
('143', 'Osmosis'),
('268', 'Particle tracking'),
('84', 'Patch-Clamp Techniques'),
('126', 'Pathology'),
('209', 'Pattern Recognition, Automated'),
('227', 'Paxillin'),
('310', 'Phagocytosis'),
('95', 'Phenotype'),
('31', 'Photography'),
('144', 'Photoperiod'),
('145', 'Photosynthesis'),
('43', 'Pilot Projects'),
('311', 'Placenta'),
('141', 'Plant'),
('146', 'Plant Leaves'),
('147', 'Plant Roots'),
('312', 'Plasmodium berghei'),
('275', 'Portal Vein'),
('148', 'Potassium'),
('76', 'Predictive Value of Tests'),
('313', 'Pregnancy'),
('314', 'Pregnancy Complications, Parasitic'),
('96', 'Probability'),
('48', 'Prognosis'),
('269', 'Programming Languages'),
('183', 'Prostatic Neoplasms'),
('161', 'Protein Subunits'),
('133', 'Protein-Serine-Threonine Kinases'),
('319', 'Proteins'),
('97', 'Publishing'),
('112', 'quantitative analysis'),
('293', 'Rabbits'),
('276', 'Radiography, Abdominal'),
('85', 'Rana pipiens'),
('86', 'Rats'),
('87', 'Rats, Sprague-Dawley'),
('44', 'Receptor, erbB-2'),
('168', 'Receptors, Cytoplasmic and Nuclear'),
('49', 'Receptors, Estrogen'),
('50', 'Receptors, Progesterone'),
('228', 'Recombinant Fusion Proteins'),
('239', 'Replication Origin'),
('24', 'Reproducibility of Results'),
('162', 'RNA Polymerase II'),
('163', 'RNA, Messenger'),
('25', 'Saccharomycetales'),
('77', 'Sarcoplasmic Reticulum'),
('196', 'scale invariance'),
('26', 'Schizosaccharomyces'),
('32', 'Science'),
('33', 'Scientific Misconduct'),
('156', 'Signal Processing, Computer-Assisted'),
('64', 'Signal Transduction'),
('188', 'slim'),
('283', 'Social Behavior'),
('6', 'Software'),
('54', 'Software Design'),
('149', 'Solubility'),
('229', 'Spectrum Analysis'),
('45', 'Staining and Labeling'),
('150', 'Starch'),
('157', 'Subcellular Fractions'),
('121', 'Testing'),
('205', 'Thalassemia'),
('106', 'Time Factors'),
('51', 'Tissue Array Analysis'),
('277', 'Tomography, X-Ray Computed'),
('262', 'Tracking'),
('263', 'Trajectory'),
('164', 'Transcription, Genetic'),
('230', 'Transfection'),
('264', 'Transforms'),
('184', 'Transplantation, Heterologous'),
('315', 'Trophoblasts'),
('193', 'Tubulin'),
('101', 'Tumor Markers, Biological'),
('206', 'Turkey'),
('320', 'User-Computer Interface'),
('219', 'Vesicular Transport Proteins'),
('284', 'Video Recording'),
('151', 'Water'),
('88', 'Xanthenes'),
('176', 'Zebrafish');

-- --------------------------------------------------------

--
-- Structure de la table `os`
--

CREATE TABLE `os` (
  `id_os` decimal(10,0) NOT NULL DEFAULT '0',
  `Name` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `os`
--

INSERT INTO `os` (`id_os`, `Name`) VALUES
('0', 'Unknown'),
('1', 'Windows'),
('2', 'Linux'),
('3', 'Mac OS X'),
('4', 'FreeBSD'),
('5', 'MATLAB'),
('6', 'Web'),
('7', 'iOS'),
('8', 'Fedora'),
('9', 'ImageJ'),
('10', 'FIJI');

-- --------------------------------------------------------

--
-- Structure de la table `process`
--

CREATE TABLE `process` (
  `id_process` decimal(10,0) NOT NULL DEFAULT '0',
  `id_entity` decimal(10,0) NOT NULL DEFAULT '0',
  `EDAM_operation` varchar(255) DEFAULT NULL,
  `EDAM_data` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `process`
--

INSERT INTO `process` (`id_process`, `id_entity`, `EDAM_operation`, `EDAM_data`) VALUES
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-- --------------------------------------------------------

--
-- Structure de la table `programminglanguage`
--

CREATE TABLE `programminglanguage` (
  `id_language` decimal(10,0) NOT NULL DEFAULT '0',
  `id_entity` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `programminglanguage`
--

INSERT INTO `programminglanguage` (`id_language`, `id_entity`) VALUES
('6', '623'),
('1', '767'),
('11', '2102'),
('5', '2108'),
('2', '2113'),
('3', '2114'),
('7', '2117'),
('8', '2118'),
('9', '2119'),
('12', '2120'),
('10', '2121'),
('4', '2122'),
('13', '2292'),
('14', '2399'),
('15', '2487'),
('16', '2492'),
('17', '2585'),
('18', '2606'),
('19', '2724'),
('20', '2725'),
('21', '2726');

-- --------------------------------------------------------

--
-- Structure de la table `softwareartifact`
--

CREATE TABLE `softwareartifact` (
  `id_software` decimal(10,0) NOT NULL DEFAULT '0',
  `id_entity` decimal(10,0) NOT NULL DEFAULT '0',
  `id_type` decimal(10,0) DEFAULT NULL,
  `License` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `softwareartifact`
--

INSERT INTO `softwareartifact` (`id_software`, `id_entity`, `id_type`, `License`) VALUES
('1', '3', NULL, NULL),
('2', '1436', NULL, NULL),
('3', '2098', NULL, NULL),
('4', '2109', NULL, NULL),
('5', '2116', NULL, NULL),
('6', '2123', NULL, NULL),
('7', '2263', NULL, NULL),
('8', '2264', NULL, NULL),
('9', '2265', NULL, NULL),
('10', '2266', NULL, NULL),
('11', '2267', NULL, NULL),
('12', '2268', NULL, NULL),
('13', '2269', NULL, NULL),
('14', '2270', NULL, NULL),
('15', '2271', NULL, NULL),
('16', '2272', NULL, NULL),
('17', '2273', NULL, NULL),
('18', '2274', NULL, NULL),
('19', '2275', NULL, NULL),
('20', '2276', NULL, NULL),
('21', '2277', NULL, NULL),
('22', '2278', NULL, NULL),
('23', '2279', NULL, NULL),
('24', '2291', NULL, NULL),
('25', '2294', NULL, NULL),
('26', '2296', NULL, NULL),
('27', '2297', NULL, NULL),
('28', '2298', NULL, NULL),
('29', '2300', NULL, NULL),
('30', '2302', NULL, NULL),
('31', '2303', NULL, NULL),
('32', '2306', NULL, NULL),
('33', '2307', NULL, NULL),
('34', '2308', NULL, NULL),
('35', '2314', NULL, NULL),
('36', '2317', NULL, NULL),
('37', '2318', NULL, NULL),
('38', '2319', NULL, NULL),
('39', '2320', NULL, NULL),
('40', '2321', NULL, NULL),
('41', '2322', NULL, NULL),
('42', '2323', NULL, NULL),
('43', '2324', NULL, NULL),
('44', '2325', NULL, NULL),
('45', '2326', NULL, NULL),
('46', '2327', NULL, NULL),
('47', '2328', NULL, NULL),
('48', '2329', NULL, NULL),
('49', '2330', NULL, NULL),
('50', '2331', NULL, NULL),
('51', '2332', NULL, NULL),
('52', '2333', NULL, NULL),
('53', '2334', NULL, NULL),
('54', '2335', NULL, NULL),
('55', '2344', NULL, NULL),
('56', '2345', NULL, NULL),
('57', '2368', NULL, NULL),
('58', '2420', NULL, NULL),
('59', '2424', NULL, NULL),
('60', '2443', NULL, NULL),
('61', '2467', NULL, NULL),
('62', '2518', NULL, NULL),
('63', '2530', NULL, NULL),
('64', '2552', NULL, NULL),
('65', '2561', NULL, NULL),
('66', '2564', NULL, NULL),
('67', '2583', NULL, NULL),
('68', '2605', NULL, NULL),
('69', '2608', NULL, NULL),
('70', '2609', NULL, NULL),
('71', '2627', NULL, NULL),
('72', '2640', NULL, NULL),
('73', '2641', NULL, NULL),
('74', '2642', NULL, NULL),
('75', '2643', NULL, NULL),
('76', '2645', NULL, NULL),
('77', '2650', NULL, NULL),
('78', '2657', NULL, NULL),
('79', '2660', NULL, NULL),
('80', '2663', NULL, NULL),
('81', '2669', NULL, NULL),
('82', '2670', NULL, NULL),
('83', '2672', NULL, NULL),
('84', '2673', NULL, NULL),
('85', '2675', NULL, NULL),
('86', '2679', NULL, NULL),
('87', '2688', NULL, NULL),
('88', '2694', NULL, NULL),
('89', '2696', NULL, NULL),
('90', '2703', NULL, NULL),
('91', '2705', NULL, NULL),
('92', '2709', NULL, NULL),
('93', '2718', NULL, NULL),
('94', '2719', NULL, NULL),
('95', '2720', NULL, NULL),
('96', '2721', NULL, NULL),
('97', '2722', NULL, NULL);

-- --------------------------------------------------------

--
-- Structure de la table `softwarelanguagerelation`
--

CREATE TABLE `softwarelanguagerelation` (
  `id_software` decimal(10,0) NOT NULL DEFAULT '0',
  `id_language` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `softwarelanguagerelation`
--

INSERT INTO `softwarelanguagerelation` (`id_software`, `id_language`) VALUES
('2', '1'),
('4', '2'),
('4', '6'),
('6', '6'),
('7', '11'),
('8', '1'),
('8', '2'),
('8', '6'),
('8', '19'),
('9', '1'),
('9', '6'),
('10', '1'),
('11', '1'),
('12', '2'),
('12', '6'),
('12', '10'),
('13', '1'),
('15', '2'),
('17', '5'),
('19', '6'),
('20', '11'),
('21', '11'),
('22', '11'),
('23', '2'),
('24', '11'),
('25', '1'),
('26', '2'),
('26', '6'),
('27', '1'),
('34', '6'),
('37', '11'),
('55', '6'),
('56', '6'),
('57', '2'),
('57', '6'),
('58', '6'),
('59', '10'),
('60', '6'),
('61', '11'),
('62', '2'),
('65', '11'),
('67', '2'),
('67', '10'),
('67', '20'),
('68', '2'),
('69', '2'),
('70', '2'),
('71', '2'),
('73', '1'),
('73', '6'),
('75', '2'),
('76', '6'),
('76', '10'),
('79', '1'),
('80', '11'),
('81', '1'),
('82', '1'),
('84', '1'),
('86', '11'),
('87', '11'),
('89', '11'),
('90', '1'),
('91', '2'),
('93', '6'),
('93', '21'),
('94', '6'),
('94', '11'),
('95', '1'),
('95', '2'),
('97', '6');

-- --------------------------------------------------------

--
-- Structure de la table `softwareosrelation`
--

CREATE TABLE `softwareosrelation` (
  `id_os` decimal(10,0) NOT NULL DEFAULT '0',
  `id_software` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `softwareosrelation`
--

INSERT INTO `softwareosrelation` (`id_os`, `id_software`) VALUES
('3', '1'),
('3', '2'),
('3', '3'),
('1436', '1'),
('1436', '2'),
('2098', '1'),
('2109', '1'),
('2109', '2'),
('2116', '1'),
('2123', '1'),
('2123', '2'),
('2123', '3'),
('2263', '1'),
('2263', '2'),
('2264', '1'),
('2264', '2'),
('2265', '1'),
('2265', '2'),
('2265', '3'),
('2266', '1'),
('2266', '2'),
('2266', '3'),
('2267', '1'),
('2267', '2'),
('2267', '3'),
('2268', '1'),
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('2269', '1'),
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('2270', '1'),
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('2271', '2'),
('2272', '1'),
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('2272', '3'),
('2272', '4'),
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('2274', '1'),
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('2307', '1'),
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('2722', '1'),
('2722', '2');

-- --------------------------------------------------------

--
-- Structure de la table `softwareprocessrelation`
--

CREATE TABLE `softwareprocessrelation` (
  `id_software` decimal(10,0) NOT NULL DEFAULT '0',
  `id_process` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `softwareprocessrelation`
--

INSERT INTO `softwareprocessrelation` (`id_software`, `id_process`) VALUES
('4', '705'),
('9', '241'),
('9', '533'),
('10', '11'),
('10', '26'),
('10', '28'),
('10', '29'),
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('77', '787'),
('85', '778');

-- --------------------------------------------------------

--
-- Structure de la table `softwareworkflowrelation`
--

CREATE TABLE `softwareworkflowrelation` (
  `id_software` decimal(10,0) NOT NULL DEFAULT '0',
  `id_workflow` decimal(10,0) NOT NULL DEFAULT '0',
  `Position` decimal(10,0) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `softwareworkflowrelation`
--

INSERT INTO `softwareworkflowrelation` (`id_software`, `id_workflow`, `Position`) VALUES
('2', '95', '0'),
('2', '99', '0'),
('2', '103', '0'),
('4', '55', '1'),
('4', '58', '1'),
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-- --------------------------------------------------------

--
-- Structure de la table `tagentityrelation`
--

CREATE TABLE `tagentityrelation` (
  `id_tag` decimal(10,0) NOT NULL DEFAULT '0',
  `id_entity` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `tagentityrelation`
--

INSERT INTO `tagentityrelation` (`id_tag`, `id_entity`) VALUES
('1', '1436'),
('1', '1442'),
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('1', '1450'),
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('2500', '2367'),
('2500', '2593'),
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('2539', '2616'),
('2540', '2013'),
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('2542', '2635'),
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('2545', '1785'),
('2546', '1785'),
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('2562', '2642'),
('2563', '2642'),
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('2566', '2643'),
('2567', '2645'),
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('2569', '2645'),
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('2612', '2700'),
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('2614', '2700'),
('2615', '2700'),
('2616', '2700'),
('2617', '2700'),
('2618', '2703'),
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('2620', '2721'),
('2621', '2721'),
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('2627', '2722');

-- --------------------------------------------------------

--
-- Structure de la table `tags`
--

CREATE TABLE `tags` (
  `id_tag` decimal(10,0) NOT NULL DEFAULT '0',
  `Name` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `tags`
--

INSERT INTO `tags` (`id_tag`, `Name`) VALUES
('1259', ''),
('764', '.1sc'),
('765', '.2'),
('766', '.2fl'),
('767', '.3'),
('768', '.4'),
('769', '.acff'),
('770', '.afm'),
('771', '.aim'),
('772', '.al3d'),
('773', '.am'),
('774', '.amiramesh'),
('775', '.apl'),
('776', '.arf'),
('777', '.avi'),
('778', '.bip'),
('779', '.bmp'),
('780', '.c01'),
('781', '.cfg'),
('782', '.cr2'),
('783', '.crw'),
('784', '.cxd'),
('785', '.czi'),
('786', '.dat'),
('787', '.dcm'),
('788', '.dicom'),
('789', '.dm2'),
('790', '.dm3'),
('791', '.dti'),
('792', '.dv'),
('793', '.eps'),
('794', '.epsi'),
('795', '.exp'),
('796', '.fdf'),
('797', '.fff'),
('798', '.ffr'),
('799', '.fits'),
('800', '.flex'),
('801', '.fli'),
('802', '.frm'),
('803', '.gel'),
('804', '.gif'),
('805', '.grey'),
('806', '.hdr'),
('807', '.hed'),
('808', '.his'),
('809', '.htd'),
('810', '.html'),
('811', '.hx'),
('812', '.ics'),
('813', '.ids'),
('814', '.img'),
('815', '.ims'),
('816', '.inr'),
('817', '.ipl'),
('818', '.ipm'),
('819', '.ipw'),
('820', '.jp2'),
('821', '.jpg'),
('822', '.jpk'),
('823', '.jpx'),
('824', '.l2d'),
('825', '.labels'),
('826', '.lei'),
('827', '.lif'),
('828', '.liff'),
('829', '.lim'),
('830', '.lsm'),
('831', '.mdb'),
('832', '.mea'),
('833', '.mnc'),
('834', '.mng'),
('835', '.mod'),
('836', '.mov'),
('837', '.mrc'),
('838', '.mrw'),
('839', '.mtb'),
('840', '.mvd2'),
('841', '.naf'),
('842', '.nd'),
('843', '.nd2'),
('844', '.ndpi'),
('845', '.nef'),
('846', '.nhdr'),
('847', '.nrrd'),
('848', '.obsep'),
('849', '.oib'),
('850', '.oif'),
('851', '.ome'),
('852', '.ome.tiff'),
('853', '.par'),
('854', '.pcx'),
('855', '.pds'),
('856', '.pgm'),
('857', '.pic'),
('858', '.pict'),
('859', '.png'),
('860', '.pnl'),
('861', '.pr3'),
('862', '.ps'),
('863', '.psd'),
('864', '.r3d'),
('865', '.raw'),
('866', '.res'),
('867', '.scn'),
('868', '.sdt'),
('869', '.seq'),
('870', '.sld'),
('871', '.sm2'),
('872', '.sm3'),
('873', '.spi'),
('874', '.stk'),
('875', '.stp'),
('876', '.svs'),
('877', '.sxm'),
('878', '.tfr'),
('879', '.tga'),
('880', '.tif'),
('881', '.tiff'),
('882', '.tnb'),
('883', '.top'),
('884', '.txt'),
('885', '.v'),
('886', '.vms'),
('887', '.vsi'),
('888', '.vws'),
('889', '.wat'),
('890', '.xdce'),
('891', '.xml'),
('892', '.xqd'),
('893', '.xqf'),
('894', '.xv'),
('895', '.xys'),
('896', '.zfp'),
('897', '.zfr'),
('898', '.zvi'),
('1780', '16 bits'),
('1279', '16-bit'),
('1125', '1d'),
('288', '2d'),
('2062', '2d 3d'),
('2260', '2D 3D nD'),
('1240', '2d histogram'),
('986', '2d invariants'),
('987', '2d khalimsky order'),
('2612', '2D stabilization'),
('2486', '2D tracking'),
('1619', '2d visualization'),
('1847', '2d/3d'),
('2303', '2Dpeak'),
('1751', '360'),
('1232', '360.0'),
('2586', '3D'),
('1009', '3d annotation'),
('1620', '3d binary object visualization'),
('595', '3d color space'),
('988', '3d geodesic reconstruction'),
('737', '3d hole closing'),
('989', '3d labelling'),
('2450', '3D landmarks'),
('1102', '3d navigation'),
('2296', '3D reconstruction'),
('2295', '3D reconstruction spines classification rendering'),
('2204', '3d reslicing'),
('2084', '3d scene'),
('2611', '3D stabilization'),
('990', '3d surface collapse'),
('2451', '3D surfaces'),
('474', '3d textures'),
('991', '3d thinning'),
('2513', '3d threshold'),
('2487', '3D tracking'),
('352', '3d viewer'),
('1580', '3d viwer'),
('2255', '3D-annotation'),
('1378', '3d-viewer'),
('1436', '3dview'),
('278', '4d'),
('707', '5d'),
('1781', '8 bits'),
('1280', '8-bit'),
('1860', 'aberration compensation'),
('1668', 'abs'),
('2197', 'absorbance'),
('1965', 'abstract types'),
('1216', 'acceleration'),
('1130', 'accurate'),
('6', 'acquisition'),
('1795', 'acquisition bleaching'),
('2011', 'acquistion'),
('2527', 'actin filament'),
('2370', 'actionbar'),
('560', 'active contour'),
('267', 'active contours'),
('2238', 'active learning'),
('2281', 'Active Mask'),
('561', 'active meshes'),
('562', 'active surface'),
('268', 'active surfaces'),
('309', 'active-learning'),
('1188', 'adaptative'),
('2184', 'adaptative thresholding'),
('501', 'adaptive'),
('2102', 'adaptive thresholding'),
('1471', 'adaptor'),
('710', 'add'),
('971', 'add noise'),
('2361', 'additive noise'),
('2315', 'adipocyte'),
('2504', 'adipose tissue cellularity'),
('913', 'administration of users'),
('1621', 'advanced 2d visualization'),
('1173', 'advert'),
('1080', 'affine'),
('1689', 'affine transform'),
('2452', 'affine transformation'),
('1339', 'affine transormation'),
('1690', 'afin'),
('2471', 'agar plate'),
('2109', 'aggregate'),
('1010', 'aggregates'),
('2332', 'aggregation'),
('2053', 'airy unit'),
('1500', 'algorithms'),
('656', 'align'),
('1760', 'align images'),
('1761', 'align planes'),
('1041', 'alignement'),
('531', 'alignment'),
('2027', 'allignment'),
('992', 'alpha dilation'),
('2235', 'alpha-expansion'),
('738', 'alternate sequential filtering'),
('1225', 'anaglyph'),
('1226', 'anaglyphs'),
('409', 'analysis'),
('2443', 'analysis feedback microscopy'),
('1520', 'analyze'),
('461', 'analyzeskeleton'),
('1917', 'anatomy'),
('1521', 'angiogenesis'),
('1094', 'angle'),
('2432', 'angular second moment'),
('2139', 'animated gif'),
('475', 'animation'),
('1614', 'anisotropic'),
('961', 'anisotropic diffusion'),
('2009', 'anisotropic filtering'),
('1095', 'annotate'),
('356', 'annotation'),
('2412', 'Anonymisation'),
('1534', 'anticlockwise'),
('2508', 'ants'),
('2381', 'Aphelion'),
('1694', 'api'),
('465', 'application'),
('2195', 'apply deformation'),
('601', 'approximate'),
('1639', 'apps'),
('2286', 'Arabidopsis'),
('547', 'area'),
('1411', 'area closing'),
('2377', 'area fraction'),
('2288', 'area measurement'),
('1412', 'area opening'),
('1413', 'area segmentation'),
('1210', 'area selection'),
('1054', 'arithmetic operators'),
('625', 'arithmetics'),
('1549', 'arma'),
('1256', 'arrow'),
('2562', 'ART'),
('2371', 'artemia'),
('2454', 'arteriole'),
('635', 'artifact'),
('1017', 'artificial cells'),
('2082', 'ascii'),
('1108', 'assemble'),
('739', 'asymetric skeleton'),
('2177', 'atlas'),
('2626', 'audit trail'),
('1776', 'auto'),
('1542', 'auto theshold'),
('484', 'auto threshold'),
('346', 'autocompletion'),
('1649', 'autocorrelation'),
('2342', 'autofocus'),
('329', 'automated'),
('1070', 'automated analysis'),
('310', 'automated tracking'),
('492', 'automatic'),
('1340', 'automatic crop'),
('914', 'automatic import'),
('915', 'automatic tagging'),
('2040', 'automatic threshold'),
('532', 'automatic thresholding'),
('1626', 'automatic tracing'),
('1997', 'automatic tracking'),
('289', 'automize'),
('711', 'average'),
('2584', 'average shape'),
('2541', 'averaging'),
('1103', 'avi'),
('1864', 'axial profile'),
('2093', 'axis transform'),
('1947', 'axon'),
('198', 'aydrajcsn'),
('619', 'b-spline'),
('1733', 'B. subtilis'),
('493', 'background'),
('2129', 'background leveling'),
('2061', 'background removal'),
('2127', 'background shading'),
('2128', 'background substraction'),
('950', 'background subtraction'),
('1729', 'bacteria'),
('2481', 'bands'),
('1341', 'barycenter'),
('1775', 'basic'),
('476', 'basic image processing'),
('1042', 'basic registration'),
('2338', 'basic script'),
('1482', 'basic statistics'),
('1379', 'basic visualization'),
('290', 'batch'),
('311', 'batch processing'),
('432', 'batch-processing'),
('2056', 'bead'),
('2065', 'beads'),
('1085', 'behavior'),
('2456', 'behavior analysis'),
('1086', 'behaviour'),
('1182', 'benchmark'),
('1545', 'benchmarking'),
('1578', 'bending'),
('1047', 'best focus'),
('993', 'beta dilation'),
('477', 'big data'),
('916', 'big images'),
('2540', 'bilateral filter'),
('2019', 'bimodal'),
('494', 'binarization'),
('529', 'binarized'),
('380', 'binary'),
('1783', 'binary image'),
('2020', 'binary image processing'),
('1739', 'binary mask'),
('1740', 'binary stack'),
('1241', 'binning'),
('917', 'bio-formats'),
('1071', 'bioconductor'),
('1885', 'bioformat'),
('433', 'bioformats'),
('2625', 'bioimage analysis'),
('1485', 'biomedical'),
('1363', 'bisector'),
('607', 'bleach'),
('1794', 'bleach correction'),
('608', 'bleaching'),
('1800', 'blend'),
('478', 'blending'),
('1005', 'blind'),
('1786', 'blind deconvolution'),
('1311', 'blob'),
('1318', 'blobs'),
('530', 'block'),
('1111', 'block matching'),
('555', 'blocks'),
('439', 'blood vessel'),
('1946', 'blood vesseles'),
('1522', 'blood vessels'),
('949', 'bloodvessel'),
('1565', 'blur'),
('534', 'blurring'),
('524', 'boolean'),
('1027', 'border'),
('1018', 'borders'),
('2267', 'bounce'),
('1535', 'boundary'),
('1211', 'bounding box'),
('1299', 'boxplot'),
('2619', 'brain'),
('1523', 'branch'),
('1524', 'branches'),
('462', 'branching'),
('1768', 'branching points'),
('2354', 'breast cancer'),
('2419', 'bridge between software'),
('452', 'brightfield'),
('2349', 'bronchi'),
('918', 'browse images'),
('443', 'browser'),
('1283', 'browsing'),
('1757', 'bspline'),
('1242', 'bubble graph'),
('1145', 'bug'),
('2117', 'bug report'),
('1249', 'building gui'),
('1250', 'button'),
('2327', 'Buttons'),
('1158', 'bwlabel'),
('366', 'c'),
('1197', 'c library wrapper'),
('1963', 'c#'),
('466', 'c++'),
('2231', 'C++ library'),
('1501', 'c++-templates'),
('1908', 'c-elegans'),
('2217', 'C. Elegans'),
('1122', 'c.elegans'),
('2537', 'C2C12 mouse stem cells'),
('2385', 'ca2+ signaling'),
('2383', 'ca2+ sparks'),
('2384', 'calcium signaling'),
('2382', 'calcium sparks'),
('1931', 'calculator'),
('636', 'calibration'),
('1169', 'calibrator'),
('537', 'caller'),
('637', 'camera'),
('726', 'camera calibration'),
('2353', 'cancer'),
('1698', 'canny'),
('1466', 'canny edge detector'),
('1176', 'canvas'),
('1131', 'capture'),
('1979', 'carl zeiss'),
('361', 'cell'),
('2364', 'cell area'),
('1575', 'cell assay'),
('1076', 'cell biology'),
('2520', 'cell boundary detection'),
('2310', 'cell counter'),
('403', 'cell counting'),
('2376', 'cell culture'),
('675', 'cell cycle'),
('1488', 'cell descriptors'),
('1808', 'cell detection'),
('404', 'cell division'),
('2180', 'cell dynamics'),
('2534', 'cell lineage'),
('2366', 'cell membrane'),
('2440', 'cell micro-array'),
('1727', 'cell migration'),
('1809', 'cell mitosis'),
('2505', 'cell morphometry'),
('1312', 'cell nuclei'),
('1166', 'cell polarization'),
('1759', 'cell profiler'),
('291', 'cell segmentation'),
('2365', 'cell shape'),
('1271', 'cell tagging'),
('292', 'cell tracking'),
('2474', 'Cell tracking and cel volume measurement'),
('1810', 'cell-based assays'),
('2141', 'cell-lineage'),
('676', 'cellh5'),
('339', 'cellprofiler'),
('312', 'cellprofiler interoperable'),
('293', 'cells'),
('1937', 'cellular components'),
('1601', 'cellular subcompartments'),
('340', 'cellular subcomponents'),
('1817', 'celsis'),
('1745', 'center of mass'),
('2512', 'centriole tracking'),
('1432', 'centroid'),
('2302', 'centromere'),
('1308', 'challenge'),
('453', 'channel'),
('514', 'chart'),
('1820', 'checkmate'),
('411', 'chess'),
('2108', 'children'),
('952', 'chromatic aberration'),
('953', 'chromatic shift'),
('503', 'chronometer'),
('721', 'cicardian'),
('2379', 'CIELAB color space'),
('1822', 'circadian'),
('1342', 'circle fit'),
('1343', 'circle identification'),
('1683', 'clahe'),
('279', 'classification'),
('2297', 'classification rendering'),
('1602', 'classifier'),
('1823', 'classify'),
('1807', 'classify cell'),
('919', 'client'),
('920', 'client-server'),
('1286', 'clip'),
('1212', 'clipper'),
('2246', 'clipping plane'),
('1536', 'clockwise'),
('1002', 'clojure'),
('1274', 'close'),
('1364', 'closeball'),
('1756', 'closed curve fitting'),
('416', 'closing'),
('444', 'cloud'),
('434', 'cluster'),
('2568', 'cluster analysis'),
('294', 'cluster support'),
('642', 'clustering'),
('1592', 'cmyk'),
('2387', 'co-localisation'),
('1945', 'co-localization percentages'),
('1944', 'co-localized voxels'),
('2430', 'Co-occurence Matrix'),
('412', 'code sample'),
('425', 'codec'),
('513', 'coder'),
('445', 'collaboration'),
('1098', 'collaborative'),
('509', 'collection'),
('2004', 'colocalisation'),
('2193', 'colocalisation correction'),
('405', 'colocalization'),
('2470', 'colonies'),
('374', 'color'),
('954', 'color alignment'),
('596', 'color analysis'),
('2114', 'color blind'),
('554', 'color code'),
('1446', 'color combination'),
('2120', 'color compensation'),
('375', 'color deconvolution'),
('597', 'color distribution'),
('598', 'color histogram'),
('1824', 'color image'),
('2116', 'color manipulation'),
('2103', 'color measurement'),
('2373', 'color normalization'),
('1387', 'color processing'),
('2104', 'color profile'),
('1486', 'color quantization'),
('2118', 'color restoration'),
('454', 'color separation'),
('455', 'color space'),
('1502', 'color space transformation'),
('1719', 'color space transformations'),
('456', 'color splitting'),
('2105', 'color stack'),
('2610', 'color substitution'),
('1960', 'color table'),
('1610', 'color thresholding'),
('457', 'color unmixing'),
('1227', 'color-coding'),
('1688', 'colortable'),
('1828', 'colour'),
('1151', 'colour conversion'),
('2198', 'colour deconvolution'),
('1829', 'colour extraction'),
('731', 'combine'),
('1447', 'combine channels'),
('1317', 'combine images'),
('1879', 'combine z slices'),
('1251', 'combo'),
('2621', 'comercial'),
('2390', 'Comet assay'),
('921', 'command line interface'),
('426', 'command line tool'),
('467', 'command-line'),
('353', 'commercial'),
('1935', 'commerical'),
('1472', 'communicator'),
('1170', 'comparator'),
('1171', 'compare'),
('712', 'complex imaginary'),
('713', 'complex modulus'),
('1414', 'component labeling'),
('1134', 'compose'),
('1837', 'composite channels'),
('427', 'compression'),
('1541', 'comptage'),
('539', 'computation'),
('367', 'compute cumulated intesity'),
('2194', 'compute deformation from beads'),
('1489', 'computefeatures'),
('2026', 'computer aided diagnosis'),
('727', 'computer vision'),
('2031', 'computer-vision'),
('2010', 'concatenation'),
('1303', 'configure'),
('2375', 'confluence'),
('609', 'confocal'),
('1077', 'confocal microscopy'),
('2462', 'ConfoMap'),
('1043', 'conformation'),
('1035', 'connected components'),
('1746', 'connected particles'),
('1159', 'connected sets'),
('620', 'consistent'),
('994', 'constrained collapse'),
('1869', 'constrast'),
('269', 'contour'),
('389', 'contour map'),
('1467', 'contrast'),
('1330', 'contrast enhancement'),
('1123', 'control'),
('899', 'conversion'),
('1803', 'convert'),
('428', 'converter'),
('643', 'convex hull'),
('972', 'convolution'),
('610', 'correction'),
('1556', 'correlation'),
('485', 'correlation analysis'),
('2461', 'correlative microscopy'),
('486', 'costes'),
('1543', 'costes algorithm'),
('2289', 'cotyledon'),
('1433', 'count'),
('1742', 'count cells'),
('1843', 'count chromosomes'),
('2079', 'count neighbours'),
('1748', 'count nuclei'),
('1743', 'count objects'),
('1806', 'counter'),
('553', 'counting'),
('2110', 'counting per'),
('1912', 'counting spots per cells'),
('602', 'countours'),
('1724', 'course'),
('1844', 'cover'),
('1162', 'creating'),
('1263', 'crop'),
('1213', 'cropping'),
('1055', 'cross correlation'),
('2259', 'cross-platform'),
('2085', 'cross-section'),
('1650', 'crosscorrelation'),
('1749', 'crosshair'),
('1597', 'csv'),
('400', 'ct'),
('1579', 'curvature'),
('2228', 'curvature calculation'),
('2605', 'curvature filters'),
('1344', 'curvatures'),
('1734', 'curve'),
('1910', 'curve analysis'),
('1735', 'curve evolution'),
('1345', 'curve extraction'),
('1661', 'curve fitting'),
('740', 'curve point detection'),
('1582', 'curve to spline conversion'),
('1346', 'curve2spline'),
('2236', 'curvelets'),
('741', 'curvilinear skeleton'),
('2559', 'custom user interface'),
('1835', 'customizable'),
('1695', 'customization'),
('694', 'cut'),
('1564', 'cutting'),
('1861', 'cutting plane'),
('1243', 'cytofluorogram'),
('446', 'cytology'),
('2439', 'Cytoo'),
('551', 'cytoplasm'),
('2526', 'cytoskeleton'),
('1830', 'dab'),
('1304', 'daemon'),
('1019', 'dalaunay triangularization'),
('1974', 'damas'),
('1338', 'darkfield'),
('1126', 'data'),
('1640', 'data analysis'),
('1962', 'data exchange'),
('900', 'data formats'),
('1976', 'data interpretation'),
('2469', 'data mining'),
('922', 'data organization'),
('1221', 'data processing'),
('923', 'data protection'),
('924', 'data sharing'),
('925', 'data storage'),
('1300', 'data visualization'),
('1295', 'data-exporter'),
('1553', 'data-importer'),
('447', 'database'),
('502', 'datatype'),
('1252', 'datatypes'),
('1275', 'deblurring'),
('557', 'debugging'),
('611', 'decay'),
('1904', 'decision tree'),
('429', 'decoder'),
('14', 'deconvolution'),
('563', 'deformable models'),
('621', 'deformation'),
('1651', 'delaunay'),
('1020', 'delaunay triangulation'),
('2086', 'delauney'),
('742', 'delete component'),
('1755', 'delineation'),
('2306', 'DeltaVision'),
('413', 'demo'),
('1627', 'dendrite'),
('1525', 'dendrites'),
('1949', 'dendritic tree'),
('1062', 'denoise'),
('669', 'denoising'),
('505', 'density'),
('313', 'density counting'),
('1652', 'density filtering'),
('2585', 'density map'),
('1137', 'density plot'),
('1118', 'deph of field'),
('2472', 'deployed'),
('732', 'deprecated'),
('1881', 'depth'),
('1873', 'depth field'),
('1876', 'depth of field'),
('1028', 'derivative'),
('1029', 'derivatives'),
('1253', 'design'),
('926', 'desktop interface'),
('2347', 'destripe'),
('1838', 'detect objects'),
('270', 'detection'),
('1696', 'developer'),
('1850', 'developmental biology'),
('1142', 'dialate'),
('1569', 'dialog generator'),
('626', 'dialogue'),
('1628', 'diameter'),
('1331', 'dicom'),
('1172', 'difference'),
('1011', 'difference of gaussian'),
('2521', 'differentials'),
('1030', 'differentiation'),
('650', 'diffraction'),
('2498', 'diffraction limited spots'),
('1773', 'diffusion'),
('1380', 'digital connectivity'),
('2074', 'digital histology'),
('2323', 'digital pathology'),
('2075', 'digital slide'),
('1381', 'digital topology'),
('417', 'dilate'),
('418', 'dilation'),
('528', 'dimensions'),
('2418', 'dip'),
('1722', 'DIPimage; introduction; matlab'),
('1537', 'direction'),
('2174', 'directional'),
('973', 'directional filter'),
('2468', 'directionalities'),
('2622', 'Dirichlet tessellation'),
('1382', 'discrete mathematics'),
('644', 'dispersion'),
('616', 'displacement'),
('390', 'display'),
('617', 'distance'),
('1934', 'distance analysis'),
('2226', 'distance calculation'),
('1327', 'distance map'),
('368', 'distance of histograms'),
('1328', 'distance transform'),
('1952', 'distances'),
('1858', 'distortion'),
('1859', 'distortion correction'),
('435', 'distributed computing'),
('599', 'distribution'),
('645', 'distribution estimation'),
('2333', 'distribution of spot distance'),
('1031', 'distro'),
('1233', 'dithering'),
('1669', 'divide'),
('714', 'division'),
('2389', 'DNA damage'),
('347', 'doc'),
('348', 'documentation'),
('1021', 'domain'),
('1805', 'dose response'),
('2503', 'dot  segmentation'),
('2501', 'dot detection'),
('2402', 'dots'),
('487', 'double labelling'),
('333', 'downsample'),
('1583', 'draw'),
('1584', 'draw a ball'),
('1774', 'draw arrow'),
('1585', 'draw curve'),
('1586', 'draw ellipse'),
('1587', 'draw field'),
('1588', 'draw line'),
('1589', 'draw rect'),
('1590', 'draw spline'),
('1591', 'draw torus'),
('1347', 'draw triangulation'),
('603', 'drawing'),
('1708', 'DRG'),
('622', 'drift'),
('1653', 'drift correction'),
('1673', 'drop'),
('2394', 'drosophila'),
('2444', 'dual acquisition'),
('1290', 'dual-screen'),
('1957', 'duration'),
('458', 'dye'),
('630', 'dynamic'),
('1920', 'dynamic range'),
('2050', 'dynamics'),
('1731', 'e-coli'),
('2140', 'e.coli'),
('1254', 'easy'),
('2324', 'eb'),
('1758', 'ebimage'),
('1478', 'ec50'),
('1701', 'eccentricity'),
('663', 'eclipse'),
('2499', 'ECMM'),
('664', 'ecology'),
('542', 'edge'),
('523', 'edge detection'),
('962', 'edge enhancement'),
('2149', 'edge perserving'),
('963', 'edge preserving'),
('1246', 'edge-preserving'),
('1855', 'edges'),
('964', 'edges-preserving'),
('1865', 'edit'),
('1012', 'editing'),
('2237', 'editing tools'),
('1124', 'editor'),
('2028', 'effcient'),
('300', 'ehswmpqyqdy'),
('341', 'eigenvalues of hessian'),
('2543', 'eigenvector'),
('623', 'elastic'),
('1112', 'elastic alignment'),
('1113', 'elastic deformation'),
('1801', 'electron microscope'),
('314', 'electron microscopy'),
('2482', 'electrophoresis'),
('518', 'elements'),
('1868', 'elevation'),
('604', 'ellipse'),
('1348', 'ellipse fit'),
('1349', 'ellipse identification'),
('2189', 'ellipsoids'),
('1099', 'em'),
('2142', 'emai generator'),
('2460', 'embryo'),
('430', 'encoder'),
('295', 'end point assays'),
('743', 'end point detection'),
('1526', 'end-point voxel'),
('1767', 'ending points'),
('2331', 'endosomes'),
('2550', 'Endothelial Tube Formation Assay'),
('2556', 'engineering'),
('1615', 'enhance'),
('1919', 'enhance contrast'),
('1109', 'enhancement'),
('334', 'enlarge'),
('2002', 'entropy'),
('376', 'eosin'),
('2407', 'epidermis'),
('1468', 'equalization'),
('1443', 'equation'),
('715', 'equivalence'),
('2355', 'ER'),
('419', 'erode'),
('420', 'erosion'),
('677', 'error correction'),
('1313', 'estimator'),
('2551', 'ETFA'),
('744', 'euclidean binary skeleton'),
('2092', 'euclideian ball'),
('678', 'event classification'),
('679', 'event detection'),
('541', 'event management'),
('1538', 'evolution'),
('1175', 'example'),
('708', 'excel'),
('1127', 'execnet'),
('1871', 'expand'),
('2438', 'expansion rate'),
('436', 'experiment design'),
('1711', 'explant'),
('1476', 'exponent'),
('612', 'exponential'),
('1799', 'exponential fitting'),
('901', 'export'),
('1296', 'exporter'),
('2005', 'expression level'),
('1048', 'extended depth of field'),
('1114', 'extended view'),
('1491', 'extensible'),
('1388', 'extensible/plugin-ins/modules'),
('1454', 'extract'),
('1039', 'ezplug'),
('1546', 'f-factor'),
('2546', 'F-function'),
('1796', 'fading'),
('2013', 'faking'),
('2564', 'fan beam projection'),
('1816', 'farenheit'),
('1036', 'fast'),
('1179', 'fast fourier transform'),
('1983', 'fast marching'),
('1629', 'fast marching algorithm'),
('1365', 'fast marching algorithms'),
('1641', 'fast prototyping'),
('2492', 'FCS'),
('1434', 'feature'),
('315', 'feature computation'),
('728', 'feature detection'),
('570', 'feature extraction'),
('1479', 'feature selection'),
('1890', 'featurej'),
('1449', 'features'),
('2216', 'Feedback and support'),
('1087', 'ffmpeg'),
('974', 'fft'),
('1948', 'fiber'),
('1933', 'fiber analysis'),
('1704', 'fiber detection'),
('2157', 'fibers'),
('2544', 'fibrils'),
('2136', 'fiducial'),
('2133', 'fiducial markers'),
('1877', 'field'),
('1148', 'figure'),
('1616', 'figure creation'),
('1149', 'figure preparation'),
('1', 'fiji'),
('2262', 'Fiji tutorial image analysis'),
('2409', 'filaggrin'),
('2604', 'filament segmentation'),
('2156', 'filament tracer'),
('1078', 'filament tracing'),
('670', 'filaments'),
('2473', 'file export'),
('1792', 'file format'),
('902', 'file formats'),
('1897', 'file import'),
('1566', 'file importer'),
('1316', 'file management'),
('2298', 'file reader'),
('2405', 'file-formats'),
('1284', 'file-importer'),
('2485', 'fileter'),
('1006', 'fill'),
('2078', 'fill hole'),
('745', 'fill holes'),
('1554', 'filler'),
('406', 'filter'),
('2159', 'filter noise'),
('1198', 'filter operations'),
('92', 'filtering'),
('571', 'filters'),
('1914', 'find'),
('564', 'find cell'),
('2277', 'find cells'),
('2278', 'find nuclei'),
('565', 'find nucleus'),
('519', 'finite'),
('520', 'finite elements'),
('1450', 'first order statistics'),
('1306', 'fish'),
('2164', 'fisheye'),
('2466', 'fit'),
('2173', 'flat zones'),
('1842', 'flatfield'),
('1049', 'flatten'),
('1469', 'flattening'),
('1492', 'flexible'),
('1285', 'flickr'),
('2395', 'flies'),
('2319', 'flim'),
('927', 'flimfit'),
('1161', 'flip'),
('1281', 'floating-point'),
('1540', 'flop'),
('2322', 'flourescence'),
('1056', 'flow'),
('1057', 'flow map'),
('2488', 'fluorecence spectroscopy'),
('401', 'fluorescence'),
('1662', 'fluorescence decay'),
('2436', 'Fluorescence in situ hybridization'),
('680', 'fluorescence microscopy'),
('2220', 'Fluorescent images'),
('1307', 'fluorescent in-situ hybridization'),
('613', 'fluorophore'),
('2396', 'fly'),
('2397', 'fly tracking'),
('1594', 'fly-through'),
('1104', 'flycam'),
('1105', 'flying camera'),
('2207', 'fmri'),
('2336', 'focal adhesion'),
('2337', 'focal adhesion dynamics'),
('2500', 'foci'),
('1050', 'focus'),
('2008', 'focus quality'),
('1051', 'focusing'),
('618', 'follow'),
('1262', 'format'),
('1058', 'format and type conversions'),
('431', 'formats'),
('2192', 'forrest'),
('928', 'forums'),
('2449', 'fossil'),
('2575', 'Fourier Ring Correlation'),
('330', 'fourier shift theory'),
('2072', 'fourier space'),
('975', 'fourier transform'),
('681', 'framework'),
('1493', 'france'),
('614', 'frap'),
('1674', 'free'),
('700', 'freeform roi'),
('280', 'freehand contour'),
('2252', 'freehand drawing'),
('2253', 'freehand ROI'),
('1680', 'frequency'),
('1681', 'frequency analysis'),
('1314', 'fret'),
('2208', 'friendly'),
('2052', 'full width at half maximum'),
('538', 'function'),
('1713', 'fundamental image processing and analysis'),
('733', 'fuse images'),
('2130', 'fusion'),
('2055', 'fwhm'),
('2545', 'G-Function'),
('2083', 'ga to latex'),
('1576', 'gabor'),
('1716', 'Gabor wavelets'),
('498', 'gallery'),
('1959', 'gamma correction'),
('1707', 'ganglion'),
('1222', 'gatan’s 3view'),
('1269', 'gate'),
('535', 'gaussian'),
('1389', 'gaussian convolution'),
('1715', 'Gaussian derivatives'),
('976', 'gaussian filter'),
('1503', 'gaussian filtering'),
('2018', 'gaussian model'),
('1063', 'gaussian noise'),
('2483', 'gel'),
('2326', 'gel analysis'),
('722', 'gene expression'),
('2514', 'gene foci'),
('2301', 'general'),
('1840', 'general filter'),
('1332', 'general image analysis'),
('1032', 'general purpose'),
('2257', 'general purpose image processing'),
('1183', 'generator'),
('1504', 'generic'),
('1505', 'generic programming'),
('1037', 'generics'),
('1415', 'geodesic dilation'),
('1366', 'geodesic distance'),
('2227', 'geodesic distances'),
('1367', 'geodesic erosion'),
('1416', 'geodesic operations'),
('1417', 'geodesic propagation'),
('995', 'geodesy'),
('1506', 'geometric transformation'),
('1718', 'geometric transformations'),
('1642', 'geometrical transformation'),
('1771', 'geometry'),
('2533', 'germband'),
('1287', 'gestures'),
('2617', 'ghost images'),
('1064', 'gibson & lanni'),
('377', 'giemsa'),
('2431', 'GLCM'),
('381', 'global'),
('2269', 'global optimization'),
('1119', 'global threshold'),
('605', 'global thresholding'),
('2279', 'Golgi'),
('2280', 'Golgi Segmentation'),
('1100', 'google maps'),
('566', 'gpu'),
('627', 'gpu support'),
('977', 'gradient'),
('1568', 'granularity'),
('1368', 'granulometry'),
('2577', 'granulometry, granularity, texture analysis, grayscale, 2d/3d'),
('701', 'graph'),
('382', 'graph cut'),
('567', 'graph-cut'),
('2234', 'graph-cuts'),
('540', 'graphic card'),
('1257', 'graphic tool'),
('1685', 'graphical'),
('2554', 'Graphical Language'),
('391', 'graphical output'),
('1390', 'graphical programming'),
('2558', 'graphical programming syntax'),
('1448', 'graphical scripting'),
('1199', 'graphical user interface'),
('1998', 'gray level morphology'),
('383', 'grayscale'),
('2124', 'grayscale morphology'),
('1906', 'grey image'),
('2081', 'greyscale'),
('1022', 'grid'),
('2033', 'grid access'),
('2037', 'grid computation'),
('1923', 'grid of images'),
('1654', 'grouping'),
('1870', 'grow'),
('2477', 'growth'),
('1675', 'gtk'),
('296', 'gui'),
('746', 'guided skeleton'),
('271', 'gwukmiygy'),
('1831', 'h&e'),
('2318', 'H&E staining'),
('2547', 'H-function'),
('1418', 'h-maxima'),
('1419', 'h-minima'),
('1138', 'h2'),
('1139', 'h2database'),
('2421', 'haematoxylin'),
('1234', 'halftone thresholding'),
('1577', 'haralick'),
('572', 'haralick features'),
('1440', 'hardware control'),
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('316', 'hdf5'),
('2524', 'head segmentation'),
('1930', 'headless'),
('1244', 'heatmap'),
('1609', 'height'),
('1420', 'height maxima'),
('1421', 'height minima'),
('1422', 'height segmentation'),
('2271', 'hello'),
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('558', 'help'),
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('956', 'hessian'),
('682', 'hidden markov models'),
('298', 'high content screening'),
('299', 'high throughput screening'),
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('684', 'high-content screening'),
('2221', 'High-throughput screening'),
('1258', 'highlight'),
('2203', 'hindlimb'),
('2427', 'Hippocampal Neurons'),
('606', 'histogram'),
('1779', 'histogram based'),
('369', 'histogram based streching'),
('370', 'histogram calculation'),
('1970', 'histogram classification'),
('2091', 'histogram distance'),
('1470', 'histogram equalization'),
('1663', 'histogram matching'),
('1797', 'histogram matching method'),
('371', 'histogram operations'),
('1655', 'histogram output'),
('2042', 'histogram partitioning'),
('2308', 'histological slices'),
('2230', 'histological stains'),
('379', 'histology'),
('2316', 'histomorphometry'),
('747', 'hit or miss'),
('1457', 'hk-means'),
('1555', 'hole'),
('16', 'holes'),
('2001', 'homogeneity'),
('748', 'homotopic cutting'),
('996', 'homotopic thinning'),
('1557', 'horn'),
('1558', 'horn-schunck'),
('342', 'hough'),
('581', 'huang'),
('2070', 'huygens'),
('2179', 'hyperstack'),
('317', 'hysteresis thresholding'),
('2493', 'ICCS'),
('2489', 'ICS'),
('5', 'icy'),
('665', 'ide'),
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('1351', 'identify parabola'),
('1352', 'identify plane'),
('2351', 'IHC'),
('301', 'ilastik'),
('2182', 'illastik'),
('536', 'illumination correction'),
('414', 'illustration'),
('1150', 'illustrator'),
('1163', 'image'),
('407', 'image acquisition'),
('354', 'image analysis'),
('1723', 'image analysis; dipimage; matlab; image processing'),
('1023', 'image annotation'),
('930', 'image annotations'),
('1993', 'image arithmetics'),
('1391', 'image calculator'),
('1544', 'image calibration'),
('685', 'image classification'),
('1052', 'image combination'),
('1676', 'image conversion'),
('978', 'image convolution'),
('931', 'image database'),
('357', 'image editing'),
('965', 'image enhancement'),
('2525', 'image ethics'),
('932', 'image export'),
('2369', 'image extraction'),
('957', 'image features'),
('1507', 'image filtering'),
('903', 'image format'),
('904', 'image formats'),
('1762', 'image fusion'),
('2145', 'image generation'),
('1925', 'image grid'),
('2223', 'image histogram'),
('1072', 'image import'),
('702', 'image inspection'),
('358', 'image manipulation'),
('1444', 'image math'),
('979', 'image measure'),
('1836', 'image merge'),
('980', 'image noising'),
('1392', 'image normalizer'),
('1445', 'image operation'),
('1924', 'image overlap'),
('2121', 'image perception'),
('86', 'image processing'),
('1875', 'image projection'),
('933', 'image publishing'),
('1508', 'image pyramid'),
('2006', 'image quality'),
('2256', 'image readers/writer'),
('2225', 'image reconstruction'),
('2569', 'image registration'),
('1509', 'image representation'),
('33', 'image sequence'),
('1004', 'image stack'),
('372', 'image streching'),
('2089', 'image summary'),
('749', 'image to graph conversion'),
('1353', 'image translation'),
('981', 'image variance'),
('695', 'image visualization'),
('1753', 'image-guided therapy'),
('573', 'image-processing'),
('4', 'imagej'),
('2100', 'imagej plugin'),
('1326', 'imagej2'),
('1736', 'images'),
('2599', 'imaging'),
('1932', 'imaris'),
('2176', 'imate stitching'),
('1393', 'imglib2'),
('905', 'import'),
('169', 'importer'),
('1437', 'imview'),
('1874', 'in focus'),
('716', 'inf'),
('2205', 'influence zone'),
('1827', 'inpector'),
('90', 'input'),
('2275', 'input field'),
('2087', 'insert image'),
('2555', 'Instrument control'),
('1369', 'integer medial axis'),
('2233', 'integer programming'),
('1966', 'integral'),
('1967', 'integral image'),
('1928', 'integration'),
('1394', 'integration platform'),
('2442', 'intelligent imaging'),
('281', 'intensity'),
('488', 'intensity based'),
('1900', 'intensity conservation equation'),
('1793', 'intensity correction'),
('1826', 'intensity correlation'),
('2039', 'intensity histogram'),
('2341', 'intensity measurement'),
('671', 'intensity presevering'),
('703', 'intensity profile'),
('2003', 'inter-class entropy'),
('1630', 'interacive'),
('415', 'interaction'),
('2590', 'interaction analysis'),
('1305', 'interaction with images'),
('272', 'interactive'),
('479', 'interactive display adjustment'),
('1059', 'interactive operators'),
('1622', 'interactive parameter manipulation'),
('1315', 'interactive segmentation'),
('468', 'interactive visualization'),
('500', 'interactve'),
('2408', 'interdigitation index'),
('1539', 'interface'),
('750', 'interior point detection'),
('1769', 'interior points'),
('1624', 'interleave'),
('582', 'intermodes'),
('1938', 'internalization'),
('511', 'internet'),
('1143', 'interoperability'),
('335', 'interpolate'),
('544', 'interpolation'),
('2239', 'interpretation'),
('1939', 'intracellular'),
('2023', 'intraclass variance'),
('2615', 'intravital imaging'),
('2616', 'intravital tissue'),
('2433', 'Inverse Difference Moment'),
('11', 'invert'),
('1223', 'io'),
('1309', 'isbi'),
('2202', 'ischemia'),
('717', 'isnull'),
('1972', 'iso'),
('1185', 'iso-surface'),
('583', 'isodata'),
('751', 'isolated point detection'),
('1354', 'isometries'),
('282', 'isosurface'),
('1969', 'isotropic scaling'),
('2094', 'isthmuses'),
('1973', 'iterative'),
('2570', 'iterative closest point'),
('1510', 'iterator'),
('1511', 'iteratoradapters'),
('469', 'itk'),
('28', 'java'),
('350', 'javadoc'),
('48', 'javascript'),
('1818', 'jfreechart'),
('657', 'jitter removal'),
('1515', 'jtreyyeil'),
('752', 'junction point detection'),
('1527', 'junction voxel'),
('506', 'jython'),
('1458', 'k-means'),
('1298', 'kalman'),
('2258', 'Kalman Filter'),
('385', 'kapur'),
('1038', 'kdtree'),
('2000', 'keep borders'),
('628', 'kelvin'),
('2532', 'keratocyte'),
('1595', 'key-frame'),
('1116', 'keyframe'),
('2250', 'keyframe editing'),
('1106', 'keyframes'),
('1888', 'keypoints'),
('997', 'khalimsky'),
('2095', 'khalimsky order'),
('998', 'khalimsky watershed'),
('2352', 'Ki67'),
('2309', 'kidney tubules'),
('686', 'kinetics'),
('2305', 'kinetochore'),
('1821', 'king'),
('1604', 'kirsch'),
('1487', 'kmeans'),
('7', 'knime'),
('1611', 'knn'),
('672', 'kuwahara'),
('1155', 'kymograph'),
('709', 'kymographe'),
('1766', 'label'),
('1003', 'label cells'),
('1839', 'label image'),
('1160', 'label objects'),
('437', 'labeling'),
('1395', 'labelling'),
('1370', 'lambda medial axis'),
('2416', 'lamellipodium'),
('1267', 'lan'),
('958', 'landmark'),
('1980', 'landmark cloud'),
('1371', 'lantuejoul skeleton'),
('1605', 'laplace'),
('982', 'laplacian'),
('1699', 'laplacian of gaussian'),
('1714', 'large data'),
('1643', 'large image block processing'),
('480', 'large image handling'),
('318', 'large images'),
('2034', 'large parameter set'),
('1802', 'large specimen'),
('687', 'large-scale'),
('1981', 'larva tracking'),
('1863', 'lateral view'),
('934', 'ldap'),
('2287', 'leaf'),
('2386', 'leaf infection'),
('319', 'learnable segmentation'),
('1670', 'least squares'),
('2530', 'Leica'),
('2463', 'Leica Map'),
('1141', 'length'),
('2357', 'length measurements'),
('1857', 'lens'),
('638', 'lens distorsion'),
('639', 'lens distortion'),
('2531', 'leukocyte'),
('392', 'level'),
('1273', 'level set'),
('568', 'level sets'),
('584', 'li'),
('1322', 'lib'),
('114', 'library'),
('2321', 'life time'),
('2465', 'lifetime'),
('545', 'ligand'),
('546', 'ligand-receptor'),
('2345', 'lighsheet'),
('1079', 'light microscopy'),
('2137', 'light-sheet'),
('1883', 'limited focus'),
('130', 'lims'),
('1911', 'line'),
('1044', 'line alignment'),
('2517', 'line detection'),
('704', 'line roi'),
('2519', 'line segmentation'),
('2539', 'line tracing'),
('410', 'lineage'),
('1732', 'lineage tracking'),
('1730', 'lineage tree'),
('1789', 'linear'),
('1720', 'linear algebra'),
('1721', 'linear and non-linear regression'),
('1200', 'linear filter'),
('1180', 'linear filtering'),
('1396', 'linear filters'),
('1985', 'linear kernel'),
('1512', 'linear regression'),
('673', 'lines'),
('2404', 'linescan'),
('1686', 'link'),
('1383', 'linux'),
('521', 'live'),
('688', 'live cell imaging'),
('408', 'live dead cells'),
('1156', 'load images'),
('906', 'loading'),
('495', 'local'),
('2299', 'local adaptive thresholding'),
('951', 'local background'),
('1355', 'local circle fitting'),
('30', 'local extrema'),
('1201', 'local structure analysis'),
('1990', 'local thickness'),
('1992', 'local threshold'),
('496', 'local thresholding'),
('302', 'localization'),
('1656', 'localization microscopy'),
('1177', 'log'),
('1178', 'logarithmic'),
('2178', 'lookup table'),
('1528', 'loop'),
('1167', 'low-level features'),
('1684', 'lsm'),
('1978', 'lsm510'),
('658', 'lucas-kanade'),
('1329', 'lung'),
('1918', 'lut'),
('2424', 'LVD'),
('2423', 'Lymphatic Vessel Density'),
('1288', 'mac'),
('135', 'machine learning'),
('320', 'machine-learning'),
('265', 'macos'),
('1929', 'macro'),
('1882', 'macroscopy'),
('2115', 'magenta'),
('1289', 'magic mouse'),
('1081', 'magnification'),
('1573', 'manager'),
('489', 'manders'),
('1942', 'manders coefficient'),
('1438', 'manipulate'),
('440', 'manual'),
('283', 'manual contour'),
('1907', 'manual delineation'),
('2254', 'manual distance measurement'),
('1954', 'manual edit'),
('359', 'manual editing'),
('646', 'manual segmentation'),
('2241', 'manual surface drawing'),
('2378', 'manual thresholding'),
('1631', 'manual tracing'),
('321', 'manual tracking'),
('1866', 'manually'),
('1667', 'manupulation'),
('1867', 'map'),
('395', 'marching cube'),
('284', 'marching cubes'),
('1045', 'mark'),
('2131', 'marker'),
('1272', 'markers'),
('108', 'mask'),
('1184', 'masking'),
('959', 'matching'),
('1168', 'math'),
('1007', 'mathematica'),
('1146', 'mathematical morphology'),
('3', 'matlab'),
('2261', 'matlab blog education examples hands-on'),
('1297', 'matlab exporter'),
('150', 'matlab importer'),
('1321', 'matlab-clone'),
('19', 'matrix'),
('2032', 'maven'),
('718', 'max'),
('1356', 'max diameters'),
('1397', 'max filter'),
('1398', 'max homogeneity'),
('585', 'maxentropy'),
('1164', 'maxima'),
('1189', 'maximum'),
('386', 'maximum correlation thresholding'),
('1697', 'maximum entropy'),
('2096', 'maximum flow'),
('983', 'maximum flows'),
('481', 'maximum intensity projection'),
('1550', 'maximum likelihood'),
('1473', 'maximum projection'),
('1190', 'maxlocal'),
('1462', 'mcib3d'),
('586', 'mean'),
('1474', 'mean projection'),
('2188', 'mean square displacement'),
('984', 'meanfilter'),
('527', 'measure'),
('1770', 'measure angle'),
('81', 'measurement'),
('550', 'measurement display'),
('303', 'measurements'),
('1096', 'measures angles'),
('1451', 'measuring'),
('1452', 'measuring intensity'),
('1953', 'measurments'),
('2507', 'medaka'),
('1372', 'medial axis'),
('52', 'median'),
('1373', 'median filter'),
('448', 'medical'),
('1276', 'medical image'),
('1333', 'medical image analysis'),
('1334', 'medical imaging'),
('42', 'membrane'),
('2367', 'membrane staining'),
('1892', 'menu'),
('543', 'mereo'),
('1463', 'mereotopology'),
('1135', 'merge'),
('2107', 'merge color'),
('2380', 'merging'),
('396', 'mesh'),
('397', 'mesh flattening'),
('398', 'mesh noising'),
('399', 'mesh smoothing'),
('2552', 'meshed network'),
('907', 'meta data'),
('221', 'metadata'),
('2484', 'Mexican Hat'),
('1423', 'meyer watershed'),
('1424', 'meyer watershed 4d'),
('266', 'mhmzmgso'),
('1088', 'mice'),
('18', 'micro manager'),
('1494', 'micro-manage'),
('1136', 'micro-manager'),
('2340', 'microa-array'),
('2467', 'microarrays'),
('2232', 'Microglia tracing'),
('1053', 'micrograph'),
('1495', 'micromanager'),
('82', 'microscope'),
('116', 'microscopy'),
('2240', 'microscopy datasets'),
('74', 'microtiter plate'),
('2475', 'microtubule'),
('2290', 'microtubule segmentation'),
('674', 'microtubules'),
('77', 'middlebury'),
('2576', 'mifobio2016'),
('2051', 'migration'),
('1441', 'min'),
('587', 'minerror'),
('2283', 'mini-library'),
('2014', 'miniature'),
('1165', 'minima'),
('1357', 'minimal rectangle'),
('588', 'minimum'),
('1995', 'minimum length'),
('1475', 'minimum projection'),
('482', 'mip'),
('2362', 'mitochondria'),
('2363', 'mitochondrial morphology'),
('343', 'mitosis'),
('1623', 'mixture'),
('387', 'mixture of gaussians'),
('935', 'mobile device access'),
('651', 'model'),
('2035', 'model analysis'),
('615', 'model fitting'),
('273', 'model-based'),
('666', 'modeling'),
('2016', 'modelism'),
('667', 'modellling'),
('2417', 'moment'),
('1490', 'moment features'),
('2426', 'moment thresholding'),
('589', 'moments'),
('512', 'monitor'),
('72', 'monitoring'),
('34', 'montage'),
('1889', 'mops'),
('624', 'morphing'),
('2413', 'morphodynamics'),
('2047', 'morphogenesis'),
('1425', 'morphological dynamics'),
('1691', 'morphological filters'),
('421', 'morphological operations'),
('517', 'morphological reconstruction'),
('1513', 'morphological transforms'),
('80', 'morphology'),
('1439', 'morphology demo'),
('1248', 'morphometric'),
('1728', 'morphometric analysis'),
('2292', 'morphometry'),
('1110', 'mosaic'),
('1115', 'mosaics'),
('1956', 'motion'),
('729', 'motion analysis'),
('1277', 'motion estimation'),
('1217', 'motion model'),
('2270', 'motion modelling'),
('1278', 'motion quantification'),
('2464', 'MountainsMap'),
('1089', 'mouse'),
('723', 'movement'),
('2021', 'movement analysis'),
('659', 'movement filtering'),
('438', 'movie'),
('2249', 'movie creation'),
('1551', 'moving average'),
('724', 'moving cell'),
('705', 'moving roi'),
('2480', 'moving stage'),
('2334', 'moving structures'),
('1090', 'mpeg'),
('1765', 'mr'),
('402', 'mri'),
('2446', 'mRNA'),
('1570', 'msd'),
('2122', 'msrcr'),
('719', 'mult'),
('1496', 'multi platform'),
('1895', 'multi raw'),
('508', 'multi touch'),
('2166', 'multi-channel'),
('2067', 'multi-core'),
('322', 'multi-dimensional'),
('1384', 'multi-dimensional images'),
('1958', 'multi-dimensional plots'),
('1848', 'multi-methods'),
('1752', 'multi-modality'),
('1644', 'multi-platform'),
('1497', 'multi-roi'),
('1191', 'multi-thread'),
('2068', 'multi-threaded'),
('323', 'multi-threading'),
('2135', 'multi-view'),
('2522', 'multicellular dynamics'),
('1971', 'multiclass'),
('1202', 'multidimensional'),
('2024', 'multilevel thresholding'),
('2572', 'multiphoton microscopy'),
('753', 'multiple point detection'),
('2388', 'multiplicity of cellular infection'),
('1477', 'multiply'),
('483', 'multiresolution'),
('2119', 'multiscale'),
('1192', 'multithreaded'),
('2573', 'multiview'),
('689', 'muti-processing'),
('56', 'mycosis'),
('2025', 'mycosis detection'),
('1455', 'mysql'),
('29', 'n-dimensional'),
('2600', 'nanoscopy'),
('2606', 'naturalization'),
('331', 'nd'),
('1203', 'nd filters'),
('1204', 'nd images'),
('2163', 'nearest neighbor'),
('569', 'need seedpoint'),
('2066', 'need theoritical psf generator'),
('2542', 'nematic tensor'),
('1709', 'nerve cells'),
('173', 'network'),
('463', 'neurite'),
('2311', 'neurite area'),
('2313', 'neurite attachment points'),
('1705', 'neurite detection'),
('2312', 'neurite endpoints'),
('1710', 'neurite length'),
('2151', 'neurite tracer'),
('1703', 'neurite tracing'),
('470', 'neuroanatomy'),
('441', 'neuron'),
('1706', 'neuron cell morpholgy'),
('1632', 'neuron reconstruction'),
('471', 'neuron tracing'),
('75', 'neurons'),
('472', 'neuroscience'),
('1235', 'newspaper'),
('2557', 'NI Vision'),
('966', 'no blur'),
('1399', 'nodes'),
('46', 'noise'),
('1567', 'noise estimation'),
('1247', 'noise reduction'),
('1193', 'noise removal'),
('1846', 'noise simulator'),
('103', 'non linear'),
('2516', 'Non Local Means'),
('1798', 'non quantitative'),
('967', 'non-linear'),
('640', 'non-linear distorsion'),
('2274', 'non-particle discrimination'),
('2196', 'non-rigid'),
('2125', 'non-uniform'),
('1205', 'nonlinear filter'),
('1514', 'nonlinear regression'),
('140', 'normalization'),
('720', 'normalize'),
('2300', 'normalized variance'),
('2264', 'Notes'),
('1120', 'nuclei'),
('2368', 'nuclei segmentation'),
('1459', 'nuclei separation'),
('362', 'nucleus'),
('1814', 'nucleus detection'),
('1073', 'nucleus segmentation'),
('2479', 'number'),
('754', 'number of neighbors'),
('574', 'numpy'),
('32', 'object'),
('1214', 'object analysis'),
('2422', 'object based colocalization'),
('755', 'object border'),
('274', 'object detection'),
('2017', 'object extraction'),
('1206', 'object generation'),
('363', 'object hierarchy'),
('304', 'object hierarcy'),
('1207', 'object measurements'),
('355', 'object overlap'),
('1941', 'object relations'),
('1319', 'object removal'),
('1460', 'object separation'),
('1940', 'object statistics'),
('1916', 'object substraction'),
('1989', 'object thickness'),
('730', 'object tracking'),
('1121', 'objects'),
('51', 'obsolete'),
('908', 'ome'),
('909', 'ome-tiff'),
('910', 'omero'),
('2211', 'omero.biobank'),
('936', 'omero.searcher'),
('2212', 'omero.webtagging'),
('2291', 'ooeheuxiw'),
('2244', 'opacity'),
('166', 'open'),
('911', 'open microscopy environment (OME)'),
('41', 'open source'),
('1400', 'open-source'),
('91', 'opencl'),
('2209', 'opencv'),
('1194', 'opengray'),
('422', 'opening'),
('2411', 'OpenSlide'),
('525', 'operation'),
('53', 'operators'),
('132', 'opertaor'),
('1323', 'opreration'),
('1060', 'optic flow'),
('641', 'optical artifact'),
('1787', 'optical density correction'),
('167', 'optical flow'),
('2598', 'optical fluctuation'),
('2580', 'optical sectioning'),
('590', 'optimal'),
('1905', 'optimization'),
('1516', 'optimized'),
('2224', 'orders topology'),
('1435', 'organelle'),
('364', 'organelles'),
('1982', 'organism tracking'),
('449', 'organization'),
('2613', 'organs'),
('1097', 'orientation'),
('696', 'orientations'),
('139', 'oriented'),
('1559', 'origin'),
('1186', 'ortho-slice'),
('2245', 'ortho-slicer'),
('697', 'orthogonal'),
('631', 'orthogonal slicing'),
('556', 'orthogonal sub image'),
('1750', 'orthogonal view'),
('632', 'orthogonal views'),
('698', 'orthoviewer'),
('88', 'otsu'),
('575', 'otsu thresholding'),
('2', 'out-of-scope'),
('1608', 'outdated'),
('275', 'outlining'),
('76', 'output'),
('1560', 'output image'),
('734', 'overlap'),
('1682', 'overlapping tiles'),
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('1498', 'package'),
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('1657', 'palm'),
('2015', 'pan and tilt'),
('1617', 'paper'),
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('756', 'parallel 2d binary skeleton'),
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('2112', 'parents'),
('351', 'parser'),
('179', 'parsing mathematical equation'),
('1984', 'partial differential equation'),
('533', 'particle'),
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('1218', 'particle tracking'),
('144', 'particles'),
('1140', 'parts'),
('1529', 'path'),
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('1483', 'pca'),
('522', 'pde'),
('181', 'pdf'),
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('145', 'pearson'),
('1943', 'pearson coefficient'),
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('1219', 'persistent movement'),
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('1692', 'perspective transform'),
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('1764', 'pet'),
('2080', 'pgm'),
('1725', 'Phagokinetic Tracks'),
('2147', 'phantom'),
('2146', 'phantom generation'),
('1040', 'phase contrast'),
('2420', 'Phase-contrast microscopy'),
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('1603', 'phenotype classification'),
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('1664', 'photobleaching'),
('1665', 'photobleaching correction'),
('2012', 'photography'),
('200', 'photoshop'),
('1612', 'picker'),
('2284', 'PIL'),
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('2077', 'pink'),
('1815', 'pipe programming'),
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('1726', 'PKT'),
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('2478', 'plant morphology'),
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('1154', 'plate'),
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('510', 'pool'),
('1951', 'position'),
('1574', 'post processing'),
('68', 'postprocessing'),
('1563', 'powerwatershed'),
('2356', 'PR'),
('1547', 'precision'),
('1977', 'prediction/correction'),
('2515', 'prefiltering'),
('735', 'prepcocessing'),
('79', 'preprocessing'),
('1596', 'presentation'),
('1790', 'preserve edges'),
('1606', 'prewitt'),
('1484', 'principal component analysis'),
('1593', 'print'),
('1236', 'printing'),
('1294', 'probabilistic'),
('384', 'probability image'),
('2591', 'process on cluster'),
('1128', 'processing'),
('2410', 'processing pipeline'),
('43', 'profile'),
('1293', 'profiler'),
('1687', 'programming'),
('155', 'programming tool'),
('937', 'projection'),
('548', 'propagate'),
('1572', 'propagation'),
('2339', 'protein'),
('577', 'protein localization'),
('65', 'protocol'),
('1646', 'prototyping'),
('125', 'protractor'),
('2414', 'protrusion'),
('70', 'provider'),
('1530', 'pruning'),
('23', 'psf'),
('2063', 'psf 3d'),
('2592', 'PSF estimation'),
('1856', 'psf generator'),
('653', 'psf model'),
('2603', 'PSF stimation'),
('2058', 'psf theoritical generator'),
('1181', 'publication'),
('223', 'publish'),
('2618', 'puncta segmentation'),
('1067', 'pure-let'),
('2210', 'pyqt'),
('1717', 'pyramids'),
('210', 'python'),
('2627', 'python package'),
('2248', 'python)'),
('691', 'qt'),
('35', 'quality control'),
('2218', 'Quantative measurement'),
('725', 'quantification'),
('1215', 'quantify'),
('1403', 'quantile filter'),
('1461', 'quantization'),
('1359', 'quasi shear rotation'),
('1819', 'queen'),
('668', 'r'),
('1374', 'radial opening'),
('1548', 'rand index'),
('2191', 'random'),
('325', 'random forest'),
('2097', 'random image'),
('1647', 'random number generator'),
('1220', 'random walk'),
('1999', 'rank order filter'),
('1375', 'rankfilter'),
('1129', 'raster'),
('1237', 'raster image'),
('1238', 'rasterize'),
('647', 'ratio'),
('2200', 'ratioed images'),
('1899', 'raw'),
('1898', 'raw image'),
('1921', 'read'),
('912', 'reader'),
('2029', 'real-time applications'),
('326', 'real-time feedback'),
('1936', 'realtionship'),
('147', 'recall'),
('97', 'receptor'),
('1157', 'reconstruct'),
('1926', 'reconstruct big images'),
('1000', 'reconstruction'),
('504', 'recorder'),
('1360', 'rectangle fit'),
('2113', 'red'),
('1228', 'red cyan glasses'),
('2372', 'redness'),
('968', 'reduction'),
('1891', 'reference points'),
('2057', 'refractive index'),
('2589', 'region cometition'),
('2602', 'region compettion'),
('1335', 'region growing'),
('2172', 'region merging'),
('2090', 'region summary'),
('2101', 'regional thresholding'),
('1325', 'regions'),
('1853', 'regions of  influence'),
('1737', 'register'),
('87', 'registration'),
('2162', 'regularity'),
('2111', 'relation'),
('117', 'relationship'),
('1144', 'relationships'),
('107', 'remote'),
('938', 'remote access'),
('1625', 'remove'),
('1320', 'remove cells'),
('969', 'remove noise'),
('526', 'remover'),
('1117', 'render'),
('224', 'rendering'),
('592', 'renyientropy'),
('1480', 'reporting'),
('2624', 'reproducibility'),
('1336', 'reproducible research'),
('336', 'resampling'),
('96', 'resize'),
('1082', 'resizing'),
('633', 'reslice'),
('1862', 'reslicing'),
('2060', 'resolution'),
('2567', 'resolution estimation'),
('654', 'response'),
('237', 'restoration'),
('2069', 'restore-tools'),
('1598', 'result'),
('1872', 'results'),
('2453', 'retina'),
('2123', 'retinex'),
('2415', 'retraction'),
('232', 'rgb'),
('123', 'rgbimage'),
('515', 'rhino'),
('1068', 'richards & wolf'),
('2494', 'RICS'),
('2518', 'ridge detection'),
('388', 'ridler-calvard'),
('1231', 'riesz'),
('1738', 'riesz transform'),
('1046', 'rigid'),
('955', 'rigid registration'),
('194', 'rigid transform'),
('516', 'ring'),
('2169', 'ripley\'s k function'),
('1700', 'roberts'),
('1091', 'rodent'),
('71', 'roi'),
('185', 'roi importer'),
('1763', 'roi measurement'),
('2251', 'ROI processing'),
('344', 'rolling ball'),
('2437', 'rosette'),
('129', 'rotate'),
('559', 'rotation'),
('2374', 'round objects'),
('2538', 'RPE cells'),
('1562', 'ruler'),
('1456', 's.pombe'),
('2448', 'sally'),
('2007', 'saturation'),
('119', 'save images'),
('2036', 'scala'),
('277', 'scalable'),
('1152', 'scale'),
('1153', 'scale bar'),
('2502', 'scale transform'),
('1083', 'scaling'),
('490', 'scatter'),
('227', 'scatter plot'),
('1245', 'scattergram'),
('491', 'scatterplot'),
('1016', 'scene animation'),
('2529', 'SCGE'),
('152', 'schunck'),
('1261', 'scientific computing'),
('1310', 'scoring'),
('2393', 'Scratch assay'),
('1133', 'screen'),
('1464', 'screen capture'),
('113', 'screen shot'),
('1074', 'screening'),
('1813', 'screening analysis'),
('67', 'script'),
('193', 'script editor'),
('2215', 'Script repository'),
('1385', 'scriptable'),
('78', 'scripting'),
('939', 'scripts'),
('940', 'scripts repository'),
('2549', 'SDI'),
('205', 'sdk'),
('941', 'search'),
('2307', 'section'),
('54', 'seed point'),
('2206', 'seeded'),
('2154', 'seeds'),
('1915', 'segement'),
('100', 'segmenetation'),
('1778', 'segment cells'),
('1777', 'segment nucleii'),
('2458', 'segmentatation'),
('24', 'segmentation'),
('1561', 'segmentation result'),
('1426', 'select component'),
('208', 'selection'),
('2560', 'semantic web'),
('442', 'semi automated'),
('1633', 'semi-automatic'),
('1634', 'semi-automatic tracing'),
('2152', 'semi-manual'),
('2441', 'sensitized emission'),
('1607', 'separable'),
('2344', 'separate'),
('549', 'separate cells'),
('1618', 'separate channels');
INSERT INTO `tags` (`id_tag`, `Name`) VALUES
('1852', 'separating cells from nuclei'),
('757', 'separating point detection'),
('2583', 'septum detection'),
('102', 'sequence'),
('758', 'sequential filter'),
('2401', 'serial section'),
('1666', 'series'),
('942', 'server'),
('943', 'server-client'),
('124', 'server/client'),
('36', 'set to background'),
('2266', 'shaking'),
('593', 'shanbag'),
('285', 'shape'),
('1909', 'shape analysis'),
('1453', 'shape descriptors'),
('39', 'shape feature'),
('307', 'shape features'),
('1671', 'shape filtering'),
('1361', 'shape fitting'),
('450', 'sharing'),
('970', 'sharp'),
('1772', 'sharp edges'),
('189', 'sharpening'),
('2143', 'shepp-logan'),
('244', 'shift'),
('83', 'shock'),
('2148', 'sholl'),
('1635', 'sholl analysis'),
('1427', 'shortest path'),
('190', 'show off'),
('1658', 'sieving'),
('160', 'sift'),
('1404', 'sigma filter'),
('149', 'signal processing'),
('1282', 'signed'),
('159', 'signed curvature'),
('2578', 'sim'),
('1678', 'similarity'),
('1679', 'similarity transform'),
('45', 'simple'),
('85', 'simulation'),
('2144', 'simulator'),
('2391', 'Single cell analysis'),
('2445', 'single molecule'),
('2528', 'single-cell gel electrophoresis'),
('202', 'sinusoid'),
('2563', 'SIRT'),
('2285', 'site-package'),
('286', 'size'),
('1672', 'size filtering'),
('55', 'skeleton'),
('759', 'skeleton decomposition'),
('1531', 'skeleton pruning'),
('760', 'skeleton smoothing'),
('170', 'skeletonization'),
('2553', 'skeletonize'),
('1386', 'skeletons'),
('1532', 'slab voxel'),
('578', 'slic superpixels'),
('26', 'slice'),
('699', 'slicer'),
('2167', 'slices'),
('634', 'slicing'),
('2076', 'slide scanner'),
('2320', 'slim'),
('13', 'slope'),
('217', 'smooted histogram'),
('89', 'smooth'),
('57', 'smoothing'),
('180', 'snakes'),
('110', 'snap'),
('1465', 'snapshot'),
('207', 'sobel'),
('1405', 'sobel filter'),
('186', 'socket'),
('1893', 'software'),
('1636', 'soma'),
('1270', 'sort'),
('1025', 'space partition'),
('1239', 'space-time plot'),
('1886', 'sparsity'),
('120', 'spatial'),
('146', 'spatial calibration'),
('2170', 'spatial colocalization'),
('648', 'spatial distribution'),
('2548', 'Spatial Distribution Index'),
('2608', 'spatial interaction analysis'),
('2609', 'spatial pattern analysis'),
('2160', 'spatial randomness'),
('2161', 'spatial statistics'),
('1092', 'specific application'),
('345', 'speckles'),
('111', 'speckling'),
('2073', 'spectral'),
('327', 'spectral decomposition'),
('40', 'speed'),
('2022', 'speed measurements'),
('1013', 'sphere'),
('2126', 'sphere structuring element'),
('1785', 'spherical aberration'),
('2273', 'spherical object'),
('174', 'spim'),
('1637', 'spine'),
('1638', 'spine tracking'),
('255', 'spines'),
('1788', 'spinning disk'),
('1174', 'splash'),
('133', 'splash screen'),
('17', 'spline'),
('1362', 'spline drawing'),
('2229', 'spline generation'),
('985', 'spline gradient'),
('1517', 'spline interpolation'),
('15', 'splines'),
('287', 'split'),
('365', 'split cells'),
('229', 'split channels'),
('1833', 'split colours'),
('1014', 'split objects'),
('1834', 'split spectral channels'),
('944', 'split view figure'),
('2607', 'split-Bregman segmentation'),
('49', 'spot'),
('1195', 'spot detection'),
('1913', 'spot detector'),
('2242', 'spot editing'),
('1955', 'spot segmentation'),
('2392', 'spot-like structures'),
('38', 'spots'),
('1599', 'spreadsheet'),
('2282', 'spyder'),
('182', 'sqlite'),
('706', 'squared roi'),
('337', 'squeeeze'),
('1084', 'squeeze'),
('27', 'ssim'),
('2400', 'ssTEM'),
('660', 'stabilization'),
('661', 'stabilize'),
('1922', 'stabilizer'),
('2134', 'stable'),
('138', 'stack'),
('162', 'stack rotation'),
('2614', 'stacks'),
('218', 'stain'),
('459', 'stain quantification'),
('460', 'stain separation'),
('1832', 'staining'),
('264', 'standard'),
('1107', 'star wars'),
('1208', 'statistical analysis'),
('1075', 'statistical modelling'),
('1825', 'statistical significance'),
('2171', 'statistical test'),
('59', 'statistics'),
('1301', 'statistics to object link'),
('2435', 'STED'),
('184', 'steerable'),
('1754', 'steerable  wavelets'),
('1291', 'stereo'),
('1229', 'stereo view'),
('1292', 'stereo vision'),
('2476', 'stereology'),
('1230', 'stereoscopic 3d'),
('1878', 'stereoscopy'),
('2509', 'stickleback'),
('2490', 'STICS'),
('736', 'stitch'),
('332', 'stitching'),
('2620', 'STL'),
('1518', 'stl-like'),
('945', 'storage'),
('1659', 'storm'),
('338', 'stretch'),
('2346', 'stripes'),
('197', 'structure'),
('1533', 'structure analysis'),
('2579', 'structured illumination microscopy'),
('1376', 'structuring element'),
('175', 'structuring element editor'),
('2219', 'Sub-cellular compartments'),
('2588', 'subcellular particles segmentation'),
('2601', 'subcellular sturtures segmentation'),
('1209', 'subpixel'),
('1255', 'substance'),
('196', 'substract'),
('2106', 'substraction'),
('187', 'subtract'),
('243', 'subtraction'),
('1660', 'super resolution'),
('2597', 'Super-resolution'),
('373', 'supervised'),
('579', 'supervised learning'),
('2183', 'supervised segmentation'),
('1613', 'support vector machines'),
('2138', 'sure'),
('1069', 'surf'),
('580', 'surf features'),
('649', 'surface'),
('1187', 'surface rendering'),
('1744', 'surfaces'),
('507', 'svm'),
('1008', 'swap dimensions'),
('2574', 'swc'),
('552', 'swimming pool'),
('233', 'swing'),
('2398', 'synapse'),
('2561', 'synapse detection'),
('1015', 'synapses'),
('2406', 'T'),
('1442', 't-projection'),
('225', 'table'),
('22', 'tag'),
('31', 'tagger'),
('946', 'tagging'),
('2523', 'tail segmentation'),
('1406', 'teams'),
('2399', 'TEM'),
('629', 'temperature'),
('2030', 'template'),
('1519', 'templates'),
('2587', 'terabyte'),
('2566', 'tesselation'),
('148', 'test'),
('360', 'text'),
('2038', 'text analysis'),
('215', 'text block'),
('195', 'textural features'),
('69', 'texture'),
('2429', 'texture analysis'),
('241', 'texture features'),
('655', 'theoretical point spread function'),
('242', 'theoretical psf'),
('1845', 'theoritical deconvolution'),
('2064', 'theoritical psf'),
('1849', 'theoritical psf generator'),
('2059', 'theory'),
('1001', 'theta-connected component labeling'),
('238', 'thick'),
('423', 'thicken'),
('1950', 'thickness'),
('424', 'thin'),
('761', 'thinning'),
('1986', 'thread'),
('2041', 'threholding'),
('66', 'threshold'),
('1782', 'threshold tests'),
('1887', 'thresholded landweber'),
('62', 'thresholding'),
('2495', 'TICCS'),
('2491', 'TICS'),
('1804', 'tiff'),
('499', 'tile'),
('1101', 'tiled'),
('203', 'tiles'),
('212', 'tiling'),
('141', 'time'),
('183', 'time delay'),
('464', 'time lapse'),
('692', 'time lapse analysis'),
('662', 'time lapse sequences'),
('177', 'time series'),
('154', 'time stamp'),
('153', 'time-lapse'),
('1812', 'time-lapse analysis'),
('693', 'time-lapse microscopy'),
('126', 'time-series'),
('2049', 'timelapse'),
('2428', 'TIRF'),
('2330', 'tissue'),
('2044', 'tissue analysis'),
('1854', 'tissue segmentation'),
('451', 'tissue slides'),
('230', 'tk/pil'),
('61', 'tnt'),
('1677', 'tomography'),
('1902', 'tool'),
('9', 'toolbox'),
('1648', 'toolboxes'),
('1337', 'toolkit'),
('25', 'tooltip'),
('214', 'top hat'),
('220', 'top-hat'),
('1196', 'tophat'),
('188', 'topography'),
('1428', 'topological watershed'),
('762', 'topologically controlled dilation'),
('763', 'topologically controlled erosion'),
('219', 'topologically correct marching cubes'),
('93', 'topology'),
('1377', 'topology controlled sequential filter'),
('171', 'touchpad'),
('112', 'toxicity'),
('222', 'tracing'),
('1093', 'track'),
('2243', 'track editing'),
('1265', 'track manager'),
('2535', 'track merging'),
('1571', 'track number'),
('1266', 'track processor'),
('2536', 'track splitting'),
('1600', 'track-importer'),
('1224', 'trackem2'),
('172', 'tracker'),
('99', 'tracking'),
('2186', 'tracking analysis'),
('2048', 'traffic'),
('176', 'train'),
('2190', 'trainable'),
('2434', 'training'),
('2185', 'trajectories analysis'),
('235', 'trajectory'),
('127', 'trakem2'),
('2447', 'transcripts'),
('209', 'transform'),
('201', 'transformation'),
('256', 'transforms'),
('142', 'translate'),
('206', 'translation'),
('151', 'translocation'),
('20', 'transpose'),
('258', 'tree-like structure'),
('594', 'triangle'),
('1851', 'triangles'),
('1026', 'triangulation'),
('2348', 'tube'),
('2153', 'tube-like structures'),
('2358', 'tubeness'),
('2155', 'tubular structures'),
('2168', 'turboreg'),
('47', 'tutorial'),
('1784', 'two photon'),
('2213', 'u-track'),
('497', 'uneven illumination'),
('252', 'ungerflow'),
('128', 'unique label'),
('247', 'unmixing'),
('168', 'unsigned'),
('2265', 'unstable'),
('60', 'untile'),
('1791', 'unwarp'),
('2350', 'unwind'),
('101', 'update'),
('213', 'upsample'),
('165', 'usability'),
('2276', 'user input'),
('1811', 'user interaction'),
('262', 'user-friendly'),
('50', 'utility'),
('1481', 'v factor'),
('2199', 'vaa3d'),
('1147', 'valley'),
('1996', 'variable number'),
('192', 'variance'),
('1407', 'variance filter'),
('2158', 'vascularization density'),
('199', 'vasculature'),
('122', 'vector graphics'),
('2045', 'vector map'),
('231', 'velocity'),
('2187', 'velocity measurements'),
('216', 'vesicle'),
('1988', 'vesicle detection'),
('2328', 'vesicle segmentation'),
('239', 'vesicles'),
('240', 'vesicules'),
('228', 'vessel'),
('1994', 'vessicle tracking'),
('2150', 'vessles'),
('249', 'vicinity'),
('137', 'video'),
('73', 'video importer'),
('2046', 'video microscopy'),
('64', 'video recorder'),
('2455', 'video tracking'),
('947', 'view images'),
('136', 'viewer'),
('1264', 'viewing'),
('2132', 'views'),
('328', 'vigra'),
('1896', 'virtual memory'),
('2403', 'virtual slide'),
('1894', 'virtual stack'),
('2272', 'virus'),
('1702', 'visual programming'),
('1961', 'visualbasic'),
('600', 'visualisation'),
('8', 'visualization'),
('2099', 'visualize'),
('248', 'visualtization'),
('2360', 'VOI'),
('2201', 'volocity'),
('118', 'volume'),
('1429', 'volume based filtering'),
('1430', 'volume closing'),
('1747', 'volume measurements'),
('2359', 'volume of interest'),
('1431', 'volume opening'),
('251', 'volume rendering'),
('1991', 'volume thickness'),
('948', 'volume viewer'),
('98', 'voronoi'),
('250', 'voronoi diagram'),
('257', 'voronoi labelling'),
('131', 'vtk'),
('2335', 'walking average'),
('143', 'warping'),
('121', 'watershed'),
('2581', 'watershed, kmeans, pca, classification, shape analysis, shape descriptors'),
('253', 'watersheds'),
('2054', 'wavelength'),
('84', 'wavelet'),
('106', 'wavelets'),
('236', 'web'),
('109', 'web browser'),
('246', 'web frontend'),
('263', 'web interface'),
('1581', 'webcam'),
('2314', 'weka'),
('156', 'well'),
('2325', 'western blots'),
('261', 'whatershed thinning'),
('2506', 'whole organism'),
('2425', 'whole slide'),
('164', 'whole-organism'),
('2317', 'whole-slide'),
('226', 'widefield'),
('2071', 'widefield microscopy'),
('63', 'widget'),
('2268', 'wiggle'),
('161', 'windows'),
('2214', 'wnd-charm'),
('44', 'workbooks'),
('37', 'workflow'),
('1408', 'workflow database'),
('308', 'workflow engine'),
('1409', 'workflow management'),
('1410', 'workflow managment'),
('260', 'workflows'),
('58', 'workspace'),
('2457', 'worm'),
('105', 'wormatlas'),
('1712', 'worms'),
('2510', 'wound closure'),
('2511', 'wound closure assay'),
('2329', 'Wound healing'),
('1927', 'write'),
('259', 'wxwidgets'),
('1268', 'x server'),
('163', 'x-server'),
('158', 'xls export'),
('21', 'xls output'),
('245', 'xml'),
('94', 'xuggler'),
('254', 'yen'),
('104', 'z'),
('191', 'z factor'),
('2181', 'z stack'),
('1880', 'z stacks'),
('178', 'z-projection'),
('12', 'z-stack'),
('2459', 'zebrafish'),
('204', 'zeiss'),
('211', 'zernike'),
('157', 'zoom');

-- --------------------------------------------------------

--
-- Structure de la table `typesoftware`
--

CREATE TABLE `typesoftware` (
  `id_type` decimal(10,0) NOT NULL DEFAULT '0',
  `Name` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `typesoftware`
--

INSERT INTO `typesoftware` (`id_type`, `Name`) VALUES
('0', 'Unknown'),
('1', 'API'),
('2', 'Application'),
('3', 'Plugin');

-- --------------------------------------------------------

--
-- Structure de la table `user`
--

CREATE TABLE `user` (
  `id_user` decimal(10,0) NOT NULL DEFAULT '0',
  `Name` varchar(255) DEFAULT NULL,
  `url` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `user`
--

INSERT INTO `user` (`id_user`, `Name`, `url`) VALUES
('0', '', NULL),
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('552', 'abria', 'http://www.vaa3d.org'),
('594', 'adufour', 'https://research.pasteur.fr/fr/member/alexandre-dufour/'),
('538', 'Alexein', 'http://cncr.nl/research_teams/vesicle_dynamics_and_synaptic_plasticity/'),
('539', 'alexjshortt', 'http://www.ucl.ac.uk'),
('223', 'anlic', NULL),
('561', 'AnthonymcGee', 'http://laustan.com/'),
('547', 'antolambert', 'https://www.uclouvain.be/eli.html'),
('548', 'aseeber', 'http://www.fmi.ch/'),
('490', 'Aurélien Dauphin', 'http://www.gdja2ip.upmc.fr/'),
('568', 'azim58', 'https://www.cnio.es/es/index.asp'),
('12', 'barry', NULL),
('84', 'bergs', NULL),
('560', 'bieylle', 'http://www.NUS.edu.sg'),
('424', 'bpavie', 'http://bio-imaging-core.be'),
('160', 'cblackburn', NULL),
('137', 'christoph_moehl', 'http://www.dzne.de/en/research/core-facilities/image-and-data-analysis-facility.html'),
('81', 'codesolorzano', NULL),
('553', 'cojoc', 'http://cobweb.cs.uga.edu/~hanbo/'),
('569', 'cyague', 'http://www.neubias.org'),
('526', 'Cyrille Billaudeau', 'http://www.micalis.fr/micalis'),
('93', 'czhang', 'http://portal.upf.edu/web/bia'),
('480', 'dhirajbhatia1@gmail.com', 'http://curie.fr'),
('533', 'Diana Gataulin', 'http://www.weizmann.ac.il/'),
('486', 'dietzc', 'http://tech.knime.org/community/image-processing'),
('2', 'dscho', NULL),
('582', 'dunay', 'http://www.neubias.org'),
('117', 'Ellen T Arena', NULL),
('469', 'Fabrice', 'http://www.bic.u-bordeaux2.fr/thewebsite/spip.php?page=sommaire&lang=fr&var_mode=recalc'),
('587', 'flevet', 'http://www.iins.u-bordeaux.fr/Florian-Levet-s-Homepage'),
('585', 'gaby', 'http://www.gabygmartins.info'),
('600', 'GiovanniCardone', 'http://www.biochem.mpg.de/en'),
('92', 'graemeball', 'http://www.lifesci.dundee.ac.uk/cast/light-and-electron-microscopy/staff/dr-graeme-ball'),
('429', 'Howardvems', 'http://acnegiovanile-rimedicontroibrufoli.bloggg.org'),
('599', 'ijimenez', 'https://www.upf.edu/'),
('565', 'imagejan', 'http://www.fmi.ch'),
('530', 'imageprocessor', 'http://www.arizona.edu'),
('564', 'imtaiky', 'https://www.researchgate.net/profile/Taiki_Adachi'),
('592', 'irene', 'https://departamento.us.es/dtsc/ver_personal.php?id=9&nombre=fondAn-garcAa-irene'),
('488', 'ivos', 'http://mosaic.mpi-cbg.de'),
('578', 'iweber', 'http://www.neubias.org'),
('532', 'jan.schmoranzer', 'http://www.fmp-berlin.info/de/research/molekulare-physiologie-und-zellbiologie/research-groups/schmoranzer/research-schmoranzer-lab.html'),
('161', 'jburel', NULL),
('577', 'jcybulska', 'http://www.neubias.org'),
('482', 'jeri', 'http://200.214.130.94/rebrats/ing/Member.php#'),
('571', 'jfernandez', 'http://www.neubias.org'),
('555', 'Jie Zhou', 'http://faculty.cs.niu.edu/~zhou/'),
('556', 'jiezhou', 'http://faculty.cs.niu.edu/~zhou/'),
('579', 'jlindblad', 'http://www.neubias.org'),
('156', 'joshmoore', 'http://openmicroscopy.org'),
('149', 'jrswedlow', NULL),
('165', 'juliafer', NULL),
('138', 'julien colombelli', 'http://adm.irbbarcelona.org/'),
('487', 'Justyna', 'http://www.le.ac.uk'),
('163', 'Karin Aumayr', NULL),
('586', 'kartiksoni23', 'http://pasteur.fr'),
('80', 'knime', NULL),
('11', 'kota', 'http://cmci.embl.de'),
('584', 'laure plantard', 'http://cfim.ku.dk/'),
('3', 'LeeKamentsky', 'http://cellprofiler.org'),
('467', 'lgelman', 'http://www.fmi.ch'),
('575', 'lpaavolainen', 'http://www.neubias.org'),
('576', 'lplantard', 'http://www.neubias.org'),
('422', 'Luciano Lucas', 'http://open.bitplane.com/'),
('83', 'luispedro', NULL),
('519', 'marc lartaud', 'http://phiv.cirad.fr/'),
('566', 'maree', 'http://www.montefiore.ulg.ac.be/~maree/'),
('444', 'marielaureb', 'http://anexplo.genotoul.fr/'),
('168', 'Markus', NULL),
('573', 'mbencina', 'http://www.neubias.org'),
('528', 'mrsheriff', 'https://www.ebi.ac.uk/about/people/rahuman-sheriff'),
('591', 'neubias', 'http://www.upf.edu'),
('423', 'Nickngiak', 'http://ccl.med.upatras.gr'),
('234', 'NickollasM', NULL),
('472', 'Nico', 'http://www.curie.fr'),
('139', 'Niko Kladt', 'http://cecad.uni-koeln.de'),
('89', 'nrey', NULL),
('572', 'nsladoje', 'http://www.neubias.org'),
('468', 'oburri', 'Http://biop.epfl.ch'),
('79', 'Ofra Golani', 'http://www.weizmann.ac.il/vet/IC/informatics/'),
('583', 'Paula.Sampaio', 'http://www.ibmc.up.pt'),
('470', 'pbankhead', 'http://blogs.qub.ac.uk/ccbg/'),
('581', 'pbianchini', 'http://www.neubias.org'),
('85', 'Perrine', 'http://pict-ibisa.curie.fr/'),
('574', 'pkankaanpaa', 'http://www.neubias.org'),
('570', 'psampaio', 'http://www.neubias.org'),
('82', 'pwalczysko', NULL),
('481', 'qianhu', 'http://www.ion.ac.cn'),
('518', 'rahulpal4582', 'http://www.utmb.edu/iutmb/'),
('78', 'Ricard Delgado-Gonzalo', NULL),
('118', 'Rocco D\'Antuono', NULL),
('550', 'rongjianli1985', 'http://www.odu.edu/#prospective'),
('4', 'ronligt', NULL),
('176', 'rosariawelshalf', NULL),
('549', 's.ji', 'https://wsu.edu/'),
('529', 'samigee3', 'https://www.unitn.it/'),
('181', 'sbesson', NULL),
('94', 'Sebastien', 'http://adm.irbbarcelona.org'),
('559', 'SelectBio-Events', 'http://www.selectbio.com'),
('13', 'simonfn', 'http://www.let-your-data-speak.com'),
('16', 'sommerc', NULL),
('164', 'spitaler', NULL),
('515', 'sprigent', 'http://www.biogenouest.org/'),
('580', 'sstanciu', 'http://www.neubias.org'),
('205', 'Stefan Terjung', 'http://www.embl.de/almf'),
('489', 'Stephane', 'http://www.institut-vision.org'),
('477', 'Stephane Jumel', 'http://www.inra.fr/'),
('542', 'StéphaneJumel', 'http://www.rennes.inra.fr/'),
('162', 'Szymon Stoma', NULL),
('551', 'taozeng', 'http://www.wsu.edu'),
('414', 'test kota', 'http://cmci.embl.de'),
('91', 'ThomasWalter', NULL),
('75', 'tischi', 'http://www.embl.de/almf'),
('120', 'tpengo', NULL),
('471', 'tstoeter', 'http://www.lin-magdeburg.de/cni'),
('103', 'ukoethe', NULL),
('426', 'volker', 'http://dev.mri.cnrs.fr'),
('1', 'webadmin', NULL),
('558', 'xiaoxiao1012', 'https://alleninstitute.org'),
('567', 'yeonkyeong lee', 'http://intern.nmbu.no/ipv'),
('557', 'yuy2016', 'https://www.janelia.org/people/yang-yu'),
('554', 'zhizhou', 'http://alleninstitute.org/about/team/staff-profile/zhi-zhou');

-- --------------------------------------------------------

--
-- Structure de la table `userskill`
--

CREATE TABLE `userskill` (
  `id_user_skill` decimal(10,0) NOT NULL DEFAULT '0',
  `Skill` varchar(255) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `userskill`
--

INSERT INTO `userskill` (`id_user_skill`, `Skill`) VALUES
('3', 'developer, analyst'),
('1436', 'User, Analyst'),
('2098', 'User, Analyst'),
('2109', 'Developer'),
('2116', 'User, Analyst'),
('2123', 'Analyst, User'),
('2263', 'Biologist / Analyst / Developer'),
('2264', 'Biologist, Analyst, Facility manager, Developer'),
('2265', 'Biologists, image analysts'),
('2266', 'All'),
('2267', 'developers'),
('2268', 'Image Analysts, Developers, Students, Scientists with Image Processing Issues, Bioligists, Chemists, Engineers'),
('2269', 'Analyst / User'),
('2270', 'analyst, no programming experience'),
('2271', 'Developers, Image Analysis, Biologist'),
('2272', 'developer, analyst'),
('2273', 'Developer / Analyst / Biologist'),
('2274', 'biologist, analyst, developer'),
('2275', 'Biologist, Analyst'),
('2276', 'Analyst, Developer'),
('2277', 'Analyst, Developer'),
('2278', 'Biologist, Analyst'),
('2279', 'Analyst / Developer'),
('2291', 'User, Analyst'),
('2294', 'developer, analyst, users'),
('2296', 'Analysts, Users'),
('2297', 'Analysts, Users'),
('2298', 'Developers, Analysts, Users'),
('2300', 'Analysts, Users'),
('2302', 'User'),
('2303', 'Analysts, Users'),
('2306', 'Developers, Analysts, Users'),
('2307', 'Developer, Analyst, User'),
('2308', 'Analyst, User.'),
('2314', 'User'),
('2317', 'User'),
('2318', 'Developers, Analysts, Users'),
('2319', 'Developers, Analysts, Users'),
('2320', 'Developer'),
('2321', '-'),
('2322', '-'),
('2323', '-'),
('2324', 'Analysts, Users'),
('2325', '-'),
('2326', 'Users'),
('2327', '-'),
('2328', '-'),
('2329', '-'),
('2330', '-'),
('2331', '-'),
('2332', '-'),
('2333', '-'),
('2334', '-'),
('2335', '-'),
('2344', 'Developer'),
('2345', 'Developer'),
('2368', 'Analyst, user, developer'),
('2420', 'User, Analyst'),
('2424', 'Developer, Analyst'),
('2443', 'Developer, Analyst'),
('2467', 'User'),
('2518', 'Analyst, User'),
('2530', 'Analyst'),
('2552', 'User'),
('2561', 'Analyst, User'),
('2564', 'User'),
('2583', 'All'),
('2605', 'Analyst, User'),
('2608', 'Unix/Linux/Mac OS X/ CYGWIN/Windows'),
('2609', 'Analysts, Users'),
('2627', 'User'),
('2640', 'Engineer, Analyst, Developer'),
('2641', 'Developer,Analyst, User'),
('2642', 'Developer, Analyst, User'),
('2643', 'Biologist / Analyst'),
('2645', 'Developer / Analyst /Biologist'),
('2650', 'Developer, Analyst, User'),
('2657', 'Developer, Analyst, User'),
('2660', 'Analyst, User'),
('2663', 'Analyst, User'),
('2669', 'Analyst, User'),
('2670', 'Analyst, User'),
('2672', 'Analyst, User'),
('2673', 'User'),
('2675', 'Analyst'),
('2679', 'User, Developer'),
('2688', 'Analyst'),
('2694', 'Analyst'),
('2696', 'Analyst'),
('2703', 'Analyst'),
('2705', 'User'),
('2709', 'Analyst, User.'),
('2718', 'Developer, Analyst, User, IT'),
('2719', 'Developer'),
('2720', 'Analyst, User'),
('2721', 'User'),
('2722', 'Developer, Analyst');

-- --------------------------------------------------------

--
-- Structure de la table `workflow`
--

CREATE TABLE `workflow` (
  `id_workflow` decimal(10,0) NOT NULL DEFAULT '0',
  `id_entity` decimal(10,0) NOT NULL DEFAULT '0'
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `workflow`
--

INSERT INTO `workflow` (`id_workflow`, `id_entity`) VALUES
('1', '2289'),
('2', '2295'),
('3', '2305'),
('4', '2310'),
('5', '2311'),
('6', '2312'),
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('13', '2352'),
('14', '2353'),
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('18', '2358'),
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('20', '2360'),
('21', '2361'),
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('25', '2365'),
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('27', '2367'),
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('29', '2370'),
('30', '2371'),
('31', '2372'),
('32', '2373'),
('33', '2374'),
('34', '2375'),
('35', '2376'),
('36', '2377'),
('37', '2378'),
('38', '2380'),
('39', '2381'),
('40', '2382'),
('41', '2384'),
('42', '2385'),
('43', '2386'),
('44', '2387'),
('45', '2388'),
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('48', '2391'),
('49', '2392'),
('50', '2393'),
('51', '2394'),
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('53', '2396'),
('54', '2401'),
('55', '2403'),
('56', '2404'),
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('58', '2406'),
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('60', '2411'),
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('64', '2415'),
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('68', '2421'),
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('70', '2423'),
('71', '2425'),
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('73', '2427'),
('74', '2429'),
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('78', '2433'),
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('80', '2435'),
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('84', '2444'),
('85', '2448'),
('86', '2453'),
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('90', '2464'),
('91', '2465'),
('92', '2466'),
('93', '2470'),
('94', '2473'),
('95', '2474'),
('96', '2475'),
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('98', '2477'),
('99', '2481'),
('100', '2483'),
('101', '2484'),
('102', '2486'),
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('105', '2494'),
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('107', '2497'),
('108', '2500'),
('109', '2503'),
('110', '2504'),
('111', '2507'),
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('114', '2511'),
('115', '2512'),
('116', '2514'),
('117', '2516'),
('118', '2517'),
('119', '2519'),
('120', '2522'),
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('122', '2524'),
('123', '2525'),
('124', '2526'),
('125', '2527'),
('126', '2529'),
('127', '2531'),
('128', '2532'),
('129', '2536'),
('130', '2537'),
('131', '2540'),
('132', '2544'),
('133', '2546'),
('134', '2547'),
('135', '2550'),
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('137', '2555'),
('138', '2557'),
('139', '2559'),
('140', '2563'),
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('143', '2568'),
('144', '2579'),
('145', '2623'),
('146', '2635'),
('147', '2637'),
('148', '2651'),
('149', '2664'),
('150', '2671'),
('151', '2713'),
('152', '2717');

-- --------------------------------------------------------

--
-- Structure de la table `workflowlanguagerelation`
--

CREATE TABLE `workflowlanguagerelation` (
  `id_workflow` decimal(10,0) NOT NULL DEFAULT '0',
  `id_language` decimal(10,0) NOT NULL DEFAULT '0',
  `Position` decimal(10,0) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `workflowlanguagerelation`
--

INSERT INTO `workflowlanguagerelation` (`id_workflow`, `id_language`, `Position`) VALUES
('55', '6', '1'),
('58', '6', '1'),
('59', '6', '1'),
('70', '6', '1'),
('82', '6', '1'),
('142', '6', '0'),
('3', '1', '0'),
('11', '1', '2'),
('18', '1', '0'),
('21', '1', '1'),
('24', '1', '0'),
('35', '1', '0'),
('40', '1', '0'),
('56', '1', '0'),
('60', '1', '0'),
('69', '1', '0'),
('82', '1', '0'),
('84', '1', '0'),
('87', '1', '0'),
('90', '1', '1'),
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('100', '1', '0'),
('117', '1', '0'),
('122', '1', '0'),
('131', '1', '0'),
('144', '1', '1'),
('145', '1', '0'),
('148', '1', '1'),
('3', '11', '1'),
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('13', '11', '0'),
('20', '11', '1'),
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('26', '11', '0'),
('45', '11', '0'),
('48', '11', '1'),
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('67', '11', '0'),
('87', '11', '1'),
('92', '11', '1'),
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('128', '11', '1'),
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('149', '11', '1'),
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('11', '2', '1'),
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('141', '17', '0'),
('95', '18', '0'),
('99', '18', '0'),
('103', '18', '0');

-- --------------------------------------------------------

--
-- Structure de la table `workflowprocessrelation`
--

CREATE TABLE `workflowprocessrelation` (
  `id_workflow` decimal(10,0) NOT NULL DEFAULT '0',
  `id_process` decimal(10,0) NOT NULL DEFAULT '0',
  `Position` decimal(10,0) DEFAULT NULL
) ENGINE=MyISAM DEFAULT CHARSET=utf8;

--
-- Contenu de la table `workflowprocessrelation`
--

INSERT INTO `workflowprocessrelation` (`id_workflow`, `id_process`, `Position`) VALUES
('2', '134', '0'),
('4', '717', '0'),
('4', '718', '0'),
('6', '360', '0'),
('6', '450', '0'),
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('8', '787', '1'),
('9', '11', '0'),
('9', '371', '1'),
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('10', '347', '0'),
('12', '760', '0'),
('17', '787', '5'),
('18', '738', '0'),
('18', '739', '0'),
('23', '481', '0'),
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('23', '787', '4'),
('24', '580', '0'),
('25', '241', '0'),
('28', '754', '0'),
('29', '161', '0'),
('29', '758', '0'),
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('31', '742', '0'),
('33', '371', '0'),
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('36', '11', '2'),
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('44', '291', '0'),
('44', '757', '0'),
('51', '244', '0'),
('61', '750', '0'),
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('61', '752', '0'),
('70', '566', '0'),
('71', '158', '2'),
('72', '559', '0'),
('73', '158', '1'),
('74', '523', '0'),
('74', '597', '0'),
('78', '206', '0'),
('80', '625', '0'),
('92', '743', '0'),
('104', '609', '0'),
('104', '741', '0'),
('113', '11', '1'),
('116', '29', '0'),
('116', '481', '1'),
('116', '754', '1'),
('116', '758', '1'),
('116', '787', '6'),
('116', '788', '1'),
('119', '163', '0'),
('122', '677', '0'),
('129', '158', '0'),
('129', '580', '1'),
('129', '745', '0'),
('141', '755', '0'),
('144', '756', '0'),
('147', '767', '1'),
('148', '774', '0'),
('149', '774', '1'),
('151', '788', '2'),
('151', '789', '0'),
('152', '788', '3');

--
-- Index pour les tables exportées
--

--
-- Index pour la table `academicpaper`
--
ALTER TABLE `academicpaper`
  ADD PRIMARY KEY (`id_paper`),
  ADD KEY `id_type` (`id_type`);

--
-- Index pour la table `authorpaperrelation`
--
ALTER TABLE `authorpaperrelation`
  ADD PRIMARY KEY (`id_author`,`id_paper`);

--
-- Index pour la table `authors`
--
ALTER TABLE `authors`
  ADD PRIMARY KEY (`id_author`);

--
-- Index pour la table `bibliotype`
--
ALTER TABLE `bibliotype`
  ADD PRIMARY KEY (`id_type`);

--
-- Index pour la table `entity`
--
ALTER TABLE `entity`
  ADD PRIMARY KEY (`id_entity`,`id_curator`),
  ADD KEY `id_user_skill` (`id_user_skill`),
  ADD KEY `id_curator` (`id_curator`);

--
-- Index pour la table `keywordpaperrelation`
--
ALTER TABLE `keywordpaperrelation`
  ADD PRIMARY KEY (`id_keyword`,`id_paper`);

--
-- Index pour la table `keywords`
--
ALTER TABLE `keywords`
  ADD PRIMARY KEY (`id_keyword`);

--
-- Index pour la table `os`
--
ALTER TABLE `os`
  ADD PRIMARY KEY (`id_os`);

--
-- Index pour la table `process`
--
ALTER TABLE `process`
  ADD PRIMARY KEY (`id_entity`,`id_process`),
  ADD KEY `id_process` (`id_process`);

--
-- Index pour la table `programminglanguage`
--
ALTER TABLE `programminglanguage`
  ADD PRIMARY KEY (`id_entity`,`id_language`),
  ADD KEY `id_language` (`id_language`);

--
-- Index pour la table `softwareartifact`
--
ALTER TABLE `softwareartifact`
  ADD PRIMARY KEY (`id_entity`,`id_software`),
  ADD KEY `id_software` (`id_software`);

--
-- Index pour la table `softwarelanguagerelation`
--
ALTER TABLE `softwarelanguagerelation`
  ADD PRIMARY KEY (`id_software`,`id_language`);

--
-- Index pour la table `softwareosrelation`
--
ALTER TABLE `softwareosrelation`
  ADD PRIMARY KEY (`id_os`,`id_software`);

--
-- Index pour la table `softwareprocessrelation`
--
ALTER TABLE `softwareprocessrelation`
  ADD PRIMARY KEY (`id_software`,`id_process`);

--
-- Index pour la table `softwareworkflowrelation`
--
ALTER TABLE `softwareworkflowrelation`
  ADD PRIMARY KEY (`id_software`,`id_workflow`);

--
-- Index pour la table `tagentityrelation`
--
ALTER TABLE `tagentityrelation`
  ADD PRIMARY KEY (`id_tag`,`id_entity`);

--
-- Index pour la table `tags`
--
ALTER TABLE `tags`
  ADD PRIMARY KEY (`id_tag`);

--
-- Index pour la table `typesoftware`
--
ALTER TABLE `typesoftware`
  ADD PRIMARY KEY (`id_type`);

--
-- Index pour la table `user`
--
ALTER TABLE `user`
  ADD PRIMARY KEY (`id_user`);

--
-- Index pour la table `userskill`
--
ALTER TABLE `userskill`
  ADD PRIMARY KEY (`id_user_skill`);

--
-- Index pour la table `workflow`
--
ALTER TABLE `workflow`
  ADD PRIMARY KEY (`id_entity`,`id_workflow`),
  ADD KEY `id_workflow` (`id_workflow`);

--
-- Index pour la table `workflowlanguagerelation`
--
ALTER TABLE `workflowlanguagerelation`
  ADD PRIMARY KEY (`id_workflow`,`id_language`);

--
-- Index pour la table `workflowprocessrelation`
--
ALTER TABLE `workflowprocessrelation`
  ADD PRIMARY KEY (`id_workflow`,`id_process`);

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