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NeuronalConnectivity_v7.2.script
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#require AcapellaAssayLanguage
//******************************************************************
// Neuronal Connectivity Analysis
//-----------------------------------------------------------------------------------
// Author: Marlies Verschuuren
// University of Antwerp
// Department of Veterinary Sciences
// Laboratory of Cell Biology and Histology
// Template: ©2012-2014 Evotec AG, Hamburg. All Rights Reserved.
// Creation Date: 2015-10-06
// Last Modified: 2019-05-14
//*******************************************************************
//v7.2
// > Derived from TRIAD_NeuroConnectivity_v7.2 (Marlies Verschuuren, University of Antwerp, [email protected])C
//***********************************************************************
//-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
//START MAIN
//-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#require AcapellaColumbusWebapp
#require AcapellaHDF5Support
//*******Include EVOTEC Procedures
//**UAntwerpen
path=__file__;
PathSplit(path | dirname);
Include(dirname&"EVT_SupportingProcedures_v7.proc")
Delete(path,dirname);
//*******Start Evaluation
EVT_StartEvaluation(maxevaluations=0)
timer::start("EVT_Main_Timer")
//*******Generate sourcedata, welltable and fieltable
GetSys(EVT_Workflow)
SetSys(EVT_Workflow=EVT_Workflow)
EVT_WellProcessing_SingleWell(sourcedata=eval("sourcedata",tbl()),fieldtable=eval("fieldtable",tbl()),welltable=eval("welltable",tbl()),allowdelayedloading=true)
//*******Get Workflow settings
Selectworkflow(fieldtable=fieldtable, welltable=welltable, sourcedata=sourcedata)
//*******Workflow as string with parameters
GetSys(EVT_Workflow)
//*******FlatFieldCorrection
set(IN_FlatfieldCorrection=(at("FlatfieldCorrection",EVT_Workflow)))
If(IN_FlatfieldCorrection)
EVT_ReduceImages_HarmonyFlatfieldCorrection(method="Background", sourcedata=sourcedata | sourcedata=sourcedata)
End()
//*******Analysis Well
set(skipWell=(length(eval("WellResults._HIDDEN_WellErrors",""))>0) or (FieldTable.Valid.sum==0))
if(!skipWell)
ConnectivityAnalysis(sourcedata=sourcedata, fieldtable=fieldtable, wellresults=wellresults,IN_FolderResultOutput=IN_FolderResultOutput)
timer::stop("EVT_Main_Timer")
set(WellResults._RAW_ProcessingDuration=duration)
//*******Output
EVT_FieldProcessing_CollectOutputs(FieldTable=fieldtable, WellResults=WellResults, IN_FolderResultOutput=IN_FolderResultOutput)
End()
//*******End Evaluation
EVT_EndEvaluation( _EVT_evaluationID)
///-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
//STOP MAIN
//-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Proc ConnectivityAnalysis(
table sourcedata inout,
table fieldtable inout,
container WellResults inout,
string IN_FolderResultOutput in,)
{
Input("IN_thresholdNuc", 500, "Manual threshold: Nuclei",type="d",description="Intensity threshold to identify nuclei from background. (Lower = larger nuclei)")
Input("IN_NucArea", 100, "Lower cutoff area (µm^2): Nuclei", type="d",description= "Minimal area (µm) of the nuclei. (Lower = smaller nuclei/debris identified)")
Input("IN_splitFactor", 0.95, "Split factor nuclei: Nuclei",type="d",description="Split factor of nuclei. (Lower = less nuclei split)")
Input("IN_NeuronalNucRoundness", 1.05, "Classification -- Cutoff roundness: Nuclei", type="d",description= "Minimal roundness of neuronal nuclei. (Lower = more irregular shaped (more) nuclei identified as neuronal)")
Input("IN_NeuronalNucArea", 140, "Classification -- Cutoff area (µm^2) : Nuclei", type="d",description= "Max area (µm) of neuronal nuclei. (Lower = smaller (less) nuclei identified as neuronal)")
Input("IN_NucPercMask", 50, "Classification -- Min % of border within mask neurites : Nuclei", type="d",description="Minimal area % of nuclei within neurite mask. (Lower = more nuclei outside network identified as neuronal)")
Input("IN_ClusterAnalysis",false,"Cluster analysis -- Run: Nuclei",type="y",description="Implement cluster analysis nuclei")
Input("In_ClusterDistance",5,"Cluster analysis -- Max distance (µm) : Nuclei", type="d", description="Max distance (µm) between nuclei in cluster. (Lower = smaller search region for neighbouring nuclei)")
Input("IN_dilationNuclei",0.75, "Dilation nuclei (µm) to create search region: Neurites",type="d",description="Dilation nuclei (µm) to generate search region")
Input("IN_scaleGaussianNeu", 5, "Rough -- Scale gaussian blur: Neurites",type="d",description="Scale of gaussian blur used to preprocess image for thresholding.")
Input("IN_thresholdNeu", 1100, "Rough -- Threshold: Neurites",type="d",description="Intensity threshold neurites. (Lower = more neurites detected)")
Input("IN_frangiStart",2, "Detail -- Minimal scale for enhancement: Neurites",type="d",description="Minimal scale neurite enhancement (Lower = smaller neurites")
Input("IN_frangiStepSize", 5, "Detail -- Step scale for enhancement : Neurites",type="d",description="Step of scales used for neurite enhancement")
Input("IN_frangiNumberSteps", 3, "Detail -- Number of scale steps for enhancement: Neurites",type="d",description="Number of different scales used for neurite enhancement")
Input("IN_frangiThreshold", 0.1, "Detail -- Threshold after enhancement: Neurites",type="d",description="Threshold after neurite enhancement (Lower = more neurites detected)")
Input("IN_spotMaxProj",false,"Max projection spot channels: Synapses",type="y",description="Implement max projection of spot channels. (N = Sharpest slice choosen)")
input("IN_spotMethod", "SER", "Method for spot detection: Synapses", "s", "Select Method for spot Detection. Allowed values: \"SER\",\"DOG\".")
input("IN_SpotAnalysis1",true,"Spot Analysis 1: Synapses",type="y",description=" Spot analysis 1 ")
input("IN_SpotAnalysis2",true,"Spot Analysis 2: Synapses",type="y",description="Spot analysis 2 ")
Input("IN_autoThresholdSpot",false,"Auto threshold: Synapses",type="y",description="Implement auto threshold for spot detection. (Y = Manual threshold will be ignored)")
Input("IN_thresholdSpot1", 0.3, "Manual threshold spot 1: Synapses", type="d",description= "Threshold spot 1 after spot enhancement. (Lower = more & larger spots detected)")
Input("IN_thresholdSpot2", 0.3, "Manual threshold spot 2: Synapses", type="d", description="Threshold spot 2 after spot enhancement.(Lower = more & larger spots detected)")
Input("IN_scaleSpot1", 1, "Scale spot 1: Synapses", type="d",description= "Scale spot enhancement channel Spot 1. (Lower = smaller spots enhanced)")
Input("IN_scaleSpot2", 1, "Scale spot 2: Synapses", type="d",description= "Scale spot enhancement channel Spot 2. (Lower = smaller spots enhanced)")
pack(InPara,"IN_*")
EVT_Helper_CollectInputs(InPara=InPara, WellResults=WellResults | WellResults )
delete(InPara)
GetSys(EVT_Workflow)
set(WF_Visualization=at("Visualization",EVT_Workflow))
set(WF_SaveResultImages=at("SaveImages",EVT_Workflow))
set(WF_SaveResults=at("SaveResults",EVT_Workflow))
set(WF_Assay="Connectivity")
channelNames="";
Foreach(0..(WellResults._Hidden_ChannelTable.@count-1), "i")
channelNames=channelNames&WellResults._Hidden_ChannelTable.channelName[i];
End()
foreach(range(0,FieldTable.@rowcount),"_field")
if(FieldTable.Valid[_field])
//-----Assign images-----//
sprintf(filename_base,"r%.2dc%.2df%.2d_",WellResults._RAW_WellRow,WellResults._RAW_WellColumn,FieldTable.Field[_field])
dbg::Log(5,"MV","Run",filename_base)
//*******MaxProj Nuclei
set(FieldResults=eval("FieldTable.FieldResults[_field]",cnt()))
TableFilter("Field==" & FieldTable.Field[_field],table=sourcedata | imagetable=table)
try()
channelNuc=WellResults._HIDDEN_Channel_Nuclei;
TableFilter("Channel==" & channelNuc, table=imagetable | imagesNuc=table)
Stat::Projection( imagesNuc.sourceimage,Method="max" | imgNuc=result)
Catch_Error()
dbg::Log(5,"MV","Error","No nuclei channel assigned")
Stop()
end()
//*******MaxProj Neurites
try()
channelNeu=WellResults._HIDDEN_Channel_Neurites;
TableFilter("Channel==" & channelNeu, table=imagetable | imagesNeu=table)
Stat::Projection( imagesNeu.sourceimage,Method="max" | imgNeu=result)
Catch_Error()
dbg::Log(5,"MV","Error","No Neurite channel assigned")
Stop()
end()
//*******Max proj or sharpest z-plane spots
if(WF_Assay=="pAMPA")
If(IN_spotMaxProjAMPA)
try()
channelSpot1=WellResults._HIDDEN_Channel_Spot1;
TableFilter("Channel==" & channelSpot1, table=imagetable | imagesSpot1=table)
Stat::Projection( imagesSpot1.sourceimage,Method="max" | imgSpot1=result)
Catch_Error()
dbg::Log(5,"MV","Error","No Spot1 channel assigned")
Stop()
end()
try()
channelSpot2=WellResults._HIDDEN_Channel_Spot2;
TableFilter("Channel==" & channelSpot2, table=imagetable | imagesSpot2=table)
Stat::Projection( imagesSpot2.sourceimage,Method="max" | imgSpot2=result)
Catch_Error()
dbg::Log(5,"MV","Error","No Spot2 channel assigned")
Stop()
end()
Else()
try()
channelSpot1=WellResults._HIDDEN_Channel_Spot1;
TableFilter("Channel==" & channelSpot1, table=imagetable | imagesSpot1=table)
stddevMax=0;
Foreach(range(0,imagesSpot1.@rowcount),"i")
stddev=imagesSpot1.sourceimage[i].@stddev;
if(stddev>stddevMax)
selPlane=i;
stddevMax=stddev;
End()
End()
imgSpot1=imagesSpot1.sourceimage[selPlane];
Catch_Error()
dbg::Log(5,"MV","Error","No spot1 channel assigned")
Stop()
end()
try()
channelSpot2=WellResults._HIDDEN_Channel_Spot2;
TableFilter("Channel==" & channelSpot2, table=imagetable | imagesSpot2=table)
imgSpot2=imagesSpot2.sourceimage[selPlane];
Catch_Error()
dbg::Log(5,"MV","Error","No spot2 channel assigned")
Stop()
end()
End()
Else()
If(IN_spotMaxProj)
if(IN_SpotAnalysis1)
try()
channelSpot1=WellResults._HIDDEN_Channel_Spot1;
TableFilter("Channel==" & channelSpot1, table=imagetable | imagesSpot1=table)
Stat::Projection( imagesSpot1.sourceimage,Method="max" | imgSpot1=result)
Catch_Error()
dbg::Log(5,"MV","Error","No Spot1 channel assigned")
Stop()
end()
End()
if(IN_SpotAnalysis2)
try()
channelSpot2=WellResults._HIDDEN_Channel_Spot2;
TableFilter("Channel==" & channelSpot2, table=imagetable | imagesSpot2=table)
Stat::Projection( imagesSpot2.sourceimage,Method="max" | imgSpot2=result)
Catch_Error()
dbg::Log(5,"MV","Error","No Spot2 channel assigned")
Stop()
end()
End()
Else()
if(IN_SpotAnalysis1)
try()
channelSpot1=WellResults._HIDDEN_Channel_Spot1;
TableFilter("Channel==" & channelSpot1, table=imagetable | imagesSpot1=table)
stddevMax=0;
Foreach(range(0,imagesSpot1.@rowcount),"i")
stddev=imagesSpot1.sourceimage[i].@stddev;
if(stddev>stddevMax)
selPlane=i;
stddevMax=stddev;
End()
End()
imgSpot1=imagesSpot1.sourceimage[selPlane];
Catch_Error()
dbg::Log(5,"MV","Error","No spot1 channel assigned")
Stop()
end()
End()
if(IN_SpotAnalysis2)
try()
channelSpot2=WellResults._HIDDEN_Channel_Spot2;
TableFilter("Channel==" & channelSpot2, table=imagetable | imagesSpot2=table)
imgSpot2=imagesSpot2.sourceimage[selPlane];
Catch_Error()
dbg::Log(5,"MV","Error","No spot2 channel assigned")
Stop()
end()
End()
End()
End()
if(at("X1",channelNames))
//*******MaxProj
try()
channelX=WellResults._HIDDEN_Channel_X1;
TableFilter("Channel==" & channelX, table=imagetable | table)
Stat::Projection( table.sourceimage,Method="max" | imgX1=result)
Catch_Error()
dbg::Log(5,"MV","Error","No X1 channel assigned")
Stop()
end()
End()
if(at("X2",channelNames))
//*******MaxProj
try()
channelX=WellResults._HIDDEN_Channel_X2;
TableFilter("Channel==" & channelX, table=imagetable | table)
Stat::Projection( table.sourceimage,Method="max" | imgX2=result)
Catch_Error()
dbg::Log(5,"MV","Error","No X2 channel assigned")
Stop()
end()
End()
if(at("X3",channelNames))
//*******MaxProj
try()
channelX=WellResults._HIDDEN_Channel_X3;
TableFilter("Channel==" & channelX, table=imagetable | table)
Stat::Projection( table.sourceimage,Method="max" | imgX3=result)
Catch_Error()
dbg::Log(5,"MV","Error","No X3 channel assigned")
Stop()
end()
End()
if(at("X4",channelNames))
//*******MaxProj
try()
channelX=WellResults._HIDDEN_Channel_X4;
TableFilter("Channel==" & channelX, table=imagetable | table)
Stat::Projection( table.sourceimage,Method="max" | imgX4=result)
Catch_Error()
dbg::Log(5,"MV","Error","No X4 channel assigned")
Stop()
end()
End()
delete(imagesNuc,imagesNeu, imagesSpot1, imagesSpot2,imagetable,channelNeu,channelNuc,channelSpot1, channelSpot2,selPlane,stddevMax,stddev,i);
//-----Calibration-----//
DistanceSizeCalibration=sqrt(AreaSizeCalibration);
//-----Texture channel images -----//
Mask(threshold=1, image=sourcedata.sourceimage[0] | mask=mask)
Mask2Stencil(Mask=mask, Method="Joint clusters" | stencil)
Stencil2Objects(Stencil=stencil | wholeImage=objects)
Texture::CalcHaralickFeatures(image=imgNuc, objects=wholeImage, stencilname="body", distance=1| textureImgNuc=objects,featurenames,NormalizedImage)
CalcIntensity(Stencil=body,Image=imgNuc,attributeName="int",CalcStdDev=true,Objects=textureImgNuc | textureImgNuc=objects)
Texture::CalcHaralickFeatures(image=imgNeu, objects=wholeImage, stencilname="body", distance=1| textureImgNeu=objects,featurenames,NormalizedImage)
CalcIntensity(Stencil=body,Image=imgNeu,attributeName="int",CalcStdDev=true,Objects=textureImgNeu | textureImgNeu=objects)
If(IN_SpotAnalysis1)
Texture::CalcHaralickFeatures(image=imgSpot1, objects=wholeImage, stencilname="body", distance=1| textureImgSpot1=objects,featurenames,NormalizedImage)
CalcIntensity(Stencil=body,Image=imgSpot1,attributeName="int",CalcStdDev=true,Objects=textureImgSpot1 | textureImgSpot1=objects)
End()
if(IN_SpotAnalysis2)
Texture::CalcHaralickFeatures(image=imgSpot2, objects=wholeImage, stencilname="body", distance=1| textureImgSpot2=objects,featurenames,NormalizedImage)
CalcIntensity(Stencil=body,Image=imgSpot2,attributeName="int",CalcStdDev=true,Objects=textureImgSpot2 | textureImgSpot2=objects)
End()
if(WF_Assay=="pAMPA")
Texture::CalcHaralickFeatures(image=imgSpot2, objects=wholeImage, stencilname="body", distance=1| textureImgSpot2=objects,featurenames,NormalizedImage)
CalcIntensity(Stencil=body,Image=imgSpot2,attributeName="int",CalcStdDev=true,Objects=textureImgSpot2 | textureImgSpot2=objects)
End()
Delete(featureNames,NormalizedImage,stencil,mask,wholeImage)
//-----Nuclear Analysis -- Detection -----//
sizeKernel=2/sqrt(areaSizeCalibration);
minAreaPx=IN_NucArea/areaSizeCalibration;
detectNuclei(imgNuc=imgNuc,threshold=IN_thresholdNuc, sizeKernel=sizeKernel, minAreaPx=minAreaPx, splitFactor=IN_splitFactor | nuclei=nuclei)
//-----Neurite Analysis -- Detection -----//
detectNeuriteMask(imgNeu=imgNeu, scaleGaussian = IN_scaleGaussianNeu, threshold=IN_thresholdNeu, frangiStart=IN_frangiStart, frangiStepSize=IN_frangiStepSize, frangiNumberSteps=IN_frangiNumberSteps, thresholdFrangi=IN_FrangiThreshold | maskNeu = maskNeu, imgFrangi=imgFrangi, imgNeu_Result_Mask=imgNeu_Result_Mask)
//-----Nuclear Analysis -- Classification -----//
if(Nuclei.@count==0)
WellResults._RAW_IndexInvalidFields=WellResults._RAW_IndexInvalidFields & (_field+1) & "-";
FieldTable.Valid[_field]=0;
else()
//-----Nuclear Analysis -- Morphology & texture -----//
setupContainer=cnt(CenterMode="Original",bArea=1,bOrigAxial=1,bOrigInten=1);
Morphology::CalcTextuMorphoFeatures(image=imgNuc, objects=nuclei, regionname= "body", RequestContainer=setupContainer| nuclei=objects)
Texture::CalcSERFeatures(image=imgNuc, objects=nuclei, stencilname="body", scale=1| nuclei=objects,SERillustrations,SERnames)
CalcArea(Stencil=border, objects=nuclei| nuclei=objects)
//-----Nuclear Analysis -- Intensity in other channels -----//
CalcIntensity(Image=imgNeu, attributeName="intensityNeu", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
if(WF_Assay=="pAMPA")
CalcIntensity(Image=imgSpot1, attributeName="intensitySpot1", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
CalcIntensity(Image=imgSpot2, attributeName="intensitySpot2", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
if(IN_SpotAnalysis1)
CalcIntensity(Image=imgSpot1, attributeName="intensitySpot1", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
if(IN_SpotAnalysis2)
CalcIntensity(Image=imgSpot2, attributeName="intensitySpot2", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
If(at("X1",channelNames))
CalcIntensity(Image=imgX1, attributeName="intensityX1", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
If(at("X2",channelNames))
CalcIntensity(Image=imgX2, attributeName="intensityX2", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
If(at("X3",channelNames))
CalcIntensity(Image=imgX3, attributeName="intensityX3", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
If(at("X4",channelNames))
CalcIntensity(Image=imgX4, attributeName="intensityX4", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
End()
//-----Filter neuronal nuclei-----//
//*******Calculate Border
CalcMassCentre(Objects=nuclei, Stencil=body | nuclei = objects )
CalcZone(Objects=nuclei, ZoneType= "equidistant" | nuclei = objects )
ZoneMask(filt_high=0, filt_low=5, objects=nuclei | nuclei = objects )
renameattr(OuterRegion=zonemask, Objects=nuclei | nuclei = objects )
CalcIntensity(Objects=nuclei, stencil=OuterRegion, image=maskNeu |nuclei = objects )
Delete(nuclei.outerzone,nuclei.zone)
nuclei_Neuronal=nuclei;
//*******Roundness
ObjectList::Split (Expression="Compactness>"&IN_NeuronalNucRoundness, Objects=nuclei_Neuronal | nuclei_Neuronal = objects, restCompactness=other)
//*******Area
threshAreaPx=IN_NeuronalNucArea/areaSizeCalibration;
ObjectList::Split (Expression="area<"&threshAreaPx,Objects=nuclei_Neuronal | nuclei_Neuronal = objects, restArea=other)
//****** Border Nuclei in neurite mask
ObjectList::Split(Expression="OuterRegion_Intensity>"&(IN_NucPercMask/100), Objects=nuclei_Neuronal | nuclei_Neuronal = objects, restMask=other )
//*******Merge Non-Neuronal
AddObjects(Extra=restCompactness, Objects=restArea| nuclei_NonNeuronal = objects)
AddObjects(Extra=restMask, Objects=nuclei_NonNeuronal | nuclei_NonNeuronal = objects)
//****Image Illustrations
CarryObjects(Stencil=restArea.body, Data="red", image=imgNuc | imgNuc_Result = image)
CarryObjects(Stencil=restCompactness.body, Data="yellow", image=imgNuc_Result | imgNuc_Result = image)
CarryObjects(Stencil=nuclei_Neuronal.body, Data="green", image=imgNuc_Result | imgNuc_Result = image)
CarryObjects(Stencil=restMask.body, Data="Magenta", image=imgNuc_Result | imgNuc_Result = image)
TextOnImage(x=(nuclei.MassCentreX), y=(nuclei.MassCentreY-30), text=("R: "&nuclei.Compactness), image=imgNuc_Result | imgNuc_Result = image )
TextOnImage(x=(nuclei.MassCentreX), y=(nuclei.MassCentreY), text=("A: "&(nuclei.Area*areaSizeCalibration)), image=imgNuc_Result | imgNuc_Result = image )
TextOnImage(x=(nuclei.MassCentreX), y=(nuclei.MassCentreY+30), text=("%: "&(nuclei.outerRegion_Intensity*100)), image=imgNuc_Result | imgNuc_Result = image )
Delete(restCompactness,restArea,restMask,restEdge)
//-----Network Analysis --Detection ----//
detectNeurites(maskNeu=maskNeu, nuclei=nuclei_neuronal | neurites=neurites)
CalcIntensity(Stencil=body,Image=imgNeu,attributeName="intensityNeu",CalcStdDev=true,Objects=neurites | neurites=objects)
CalcIntensity(image=neurites.body.@array, attributeName="IntensityMask", Stencil=body, CalcStdDev=true, Objects=nuclei | nuclei=objects)
CalcIntensity(image=neurites.body.@array, attributeName="IntensityMask", Stencil=body, CalcStdDev=true, Objects=nuclei_Neuronal | nuclei_Neuronal=objects)
CalcIntensity(image=neurites.body.@array, attributeName="IntensityMask", Stencil=body, CalcStdDev=true, Objects=nuclei_NonNeuronal | nuclei_NonNeuronal=objects)
//-----Nuclear Analysis -- Clustering ----//
if(IN_ClusterAnalysis)
//-----Distance Image-----//
MaskToDistanceImage(ObjectMask=nuclei_NonNeuronal.body, MaxDistance=100 | EDM = distanceimage)
//-----Cluster Analysis-----//
if(nuclei.@count>0)
ClusterAnalysis(Objects=nuclei, IN_max_dist=(IN_ClusterDistance/distanceSizeCalibration) | nuclei = objects, clustersNuclei = clusters)
NearestNeighbourDistance(StencilName=body,objects=nuclei,MaximumDistance=(imgNuc.@width/5)| nuclei = objects)
if(nuclei_Neuronal.@count>0)
ClusterAnalysis(Objects=nuclei_Neuronal, IN_max_dist=(IN_ClusterDistance/distanceSizeCalibration) | nuclei_Neuronal = objects, clustersNuclei_Neuronal = clusters)
NearestNeighbourDistance(StencilName=body,objects=nuclei_Neuronal,MaximumDistance=(imgNuc.@width/3)| nuclei_Neuronal = objects)
Else()
clustersNuclei_Neuronal = nuclei_neuronal;
nuclei_neuronal.NeighbourDistance=vec();
nuclei_neuronal.NumberOfObjectsInCLuster=vec();
End()
if(nuclei_NonNeuronal.@count>0)
ClusterAnalysis(Objects=nuclei_NonNeuronal, IN_max_dist=(IN_ClusterDistance/distanceSizeCalibration) | nuclei_NonNeuronal = objects, clustersNuclei_NonNeuronal = clusters)
NearestNeighbourDistance(StencilName=body,objects=nuclei_NonNeuronal,MaximumDistance=(imgNuc.@width/5)| nuclei_NonNeuronal = objects)
Else()
clustersNuclei_NonNeuronal = nuclei_Nonneuronal;
nuclei_NonNeuronal.NeighbourDistance=vec();
nuclei_Nonneuronal.NumberOfObjectsInCLuster=vec();
End()
Else()
clustersNuclei = nuclei_neuronal;
nuclei.NeighbourDistance=vec();
nuclei.NumberOfObjectsInCLuster=vec();
End()
End()
//------- Skeleton Neurites with exclusion nuclei
if(neurites.body.@array.@mean>0)
Skeleton(imgNeu=imgNeu, neurites=neurites, nuclei= nuclei_neuronal, dilationPx = IN_dilationNuclei*(-1)/distanceSizeCalibration, minAreaPx=5/DistanceSizeCalibration | skeleton=skeleton)
Else()
skeleton=neurites;
skeleton.skeleton=neurites.body;
skeleton.dendrite_maxwidth=vec(0);
skeleton.dendrite_meanwidth=vec(0);
skeleton.dendrite_medianwidth=vec(0);
skeleton.dendrite_minwidth=vec(0);
skeleton.skeleton_node_count=vec(0);
End()
//-----Search Region: dilate + exclude neuronal nuclei and artefacts in staining pre/post----- + Intesntity measurements//
if(!IN_SpotAnalysis1)
searchRegion(neurites=neurites, nuclei=nuclei_neuronal, dilationPx = IN_dilationNuclei*(-1)/distanceSizeCalibration, minAreaPx = 5/areaSizeCalibration | neurites = neurites)
CalcIntensity(Stencil=searchRegion, Image=imgNeu, AttributeName="intensityNeu",Objects=neurites | neurites=objects)
Else()
if(!IN_SpotAnalysis2)
searchRegion1Spot(neurites=neurites, nuclei=nuclei_neuronal, img1=imgSpot1, dilationPx = IN_dilationNuclei*(-1)/distanceSizeCalibration, minAreaPx = 5/areaSizeCalibration | neurites = neurites)
CalcIntensity(Stencil=searchRegion, Image=imgSpot1, AttributeName="intensitySpot1",Objects=neurites | neurites=objects)
CalcIntensity(Stencil=searchRegion, Image=imgNeu, AttributeName="intensityNeu",Objects=neurites | neurites=objects)
If(WF_Assay=="pAMPA")
CalcIntensity(Stencil=searchRegion, Image=imgSpot2, AttributeName="intensitySpot2",Objects=neurites | neurites=objects)
End()
Else()
searchRegion2Spot(neurites=neurites, nuclei=nuclei_neuronal, img1=imgSpot1, img2=imgSpot2, dilationPx =IN_dilationNuclei*(-1)/distanceSizeCalibration, minAreaPx = 5/areaSizeCalibration | neurites = neurites)
End()
End()
If(at("X1",channelNames))
CalcIntensity(Image=imgX1, attributeName="intensityX1", Stencil=searchRegion, CalcStdDev=true, Objects=neurites | neurites=objects)
End()
If(at("X2",channelNames))
CalcIntensity(Image=imgX2, attributeName="intensityX2", Stencil=searchRegion, CalcStdDev=true, Objects=neurites | neurites=objects)
End()
If(at("X3",channelNames))
CalcIntensity(Image=imgX3, attributeName="intensityX3", Stencil=searchRegion, CalcStdDev=true, Objects=neurites | neurites=objects)
End()
If(at("X4",channelNames))
CalcIntensity(Image=imgX4, attributeName="intensityX4", Stencil=searchRegion, CalcStdDev=true, Objects=neurites | neurites=objects)
End()
//-----Spot Detection & parameter extraction-----//
//*******SpotDetection & Intensity measurements
If(IN_SpotAnalysis1)
SpotDetection(img=imgSpot1, method=IN_spotMethod, spotDistance=0.30/distanceSizeCalibration, iterations=0.75/distanceSizeCalibration, scale=IN_scaleSpot1, autoThreshold=IN_autoThresholdSpot, threshold=IN_thresholdSpot1, searchRegion=neurites.searchRegion.@array| spot1 = objects)
CalcIntensity(Stencil=body, image=imgSpot1, objects=spot1, AttributeName="Intensity" | spot1 = objects)
NOT(image=spot1.body.@mask.@array | image)
AND(mask=neurites.searchRegion, image=image | image)
Stencil::CreateFromArray(Array=image, ObjectCount=1 | stencil )
stencil2objects(Stencil | temp=objects)
CalcIntensity(Stencil=body, Image=imgSpot1, AttributeName="intensityBg",Objects=temp | temp=objects)
set(neurites.intensityBgSpot1=temp.intensityBg)
CalcIntensity(Stencil=searchRegion, Image=imgSpot1, AttributeName="intensitySpot1",Objects=neurites | neurites=objects)
CalcIntensity(Stencil=searchRegion, Image=imgNeu, AttributeName="intensityNeu",Objects=neurites | neurites=objects)
CalcArea(Stencil=body, objects=spot1| spot1 = objects)
End()
If(IN_SpotAnalysis2)
SpotDetection(img=imgSpot2, method=IN_spotMethod, spotDistance=0.30/distanceSizeCalibration, iterations=0.75/distanceSizeCalibration, scale=IN_scaleSpot2, autoThreshold=IN_autoThresholdSpot, threshold=IN_thresholdSpot2, searchRegion=neurites.searchRegion.@array| spot2 = objects)
CalcIntensity(Stencil=body, image=imgSpot2, objects=spot2, AttributeName="Intensity" | spot2 = objects)
NOT(image=spot2.body.@mask.@array | image)
AND(mask=neurites.searchRegion, image=image | image)
Stencil::CreateFromArray(Array=image, ObjectCount=1 | stencil )
stencil2objects(Stencil | temp=objects)
CalcIntensity(Stencil=body, Image=imgSpot2, AttributeName="intensityBg",Objects=temp | temp=objects)
set(neurites.intensityBgSpot2=temp.intensityBg)
CalcIntensity(Stencil=searchRegion, Image=imgSpot2, AttributeName="intensitySpot2",Objects=neurites | neurites=objects)
CalcArea(Stencil=body, objects=spot2| spot2 = objects)
End()
//*******Object-based colocalisation: Measure overlap through intensity measure in binary mask (.body.@mask.@array)
If(IN_SpotAnalysis2)
//*******Overlap entire field
Mul(x=Spot1.body.@mask.@array, y=spot2.body.@mask.@array, result_type="y_type" | result)
similarityIndex=(2*result.@sum)/(Spot1.body.@mask.@array.@sum + spot2.body.@mask.@array.@sum);
//*******Overlap for every spot
CalcIntensity(Stencil=body, image=spot2.body.@mask.@array,objects=Spot1,AttributeName="Overlap" | Spot1 = objects)
CalcIntensity(Stencil=body, image=Spot1.body.@mask.@array,objects=spot2,AttributeName="Overlap" | spot2 = objects)
//*******Count overlapping spots = synapses
ObjectFilter("overlap>"&0, objects=spot1 | Synapse1 = objects)
ObjectFilter("overlap>"&0, objects=spot2 | Synapse2 = objects)
Or(mask=synapse1.body.@mask.@array, image=synapse2.body.@mask.@array | image)
Mask(threshold=1, image=image | mask)
Mask2Stencil(Mask=mask, Method="Joint clusters" | stencil)
Stencil2Objects(Stencil=stencil | synapses=objects)
CalcIntensity(Stencil=body, image=imgSpot1,objects=Synapses,AttributeName="IntensityImgSpot1" | Synapses = objects)
CalcIntensity(Stencil=body, image=imgSpot2,objects=Synapses,AttributeName="IntensityImgSpot2" | Synapses = objects)
CalcAttr(Attrname="intRatio", Formula=intensityImgSpot1/intensityImgSpot2, Objects=Synapses | Synapses = objects)
//*******Calculate percentage spots for which overlap > threshold overlap
spot1PercWithOverlap=Synapse1.@count/Spot1.@count*100;
spot2PercWithOverlap=Synapse2.@count/spot2.@count*100;
Delete(Synapse1,Synapse2,image,mask,stencil)
//*******Intensity-based colocalisation
pearsonCorr(ch1 = imgSpot1, ch2 = imgSpot2, restrictiveStencil = neurites.searchRegion | pearsonCorr, pearsonMap)
End()
//-----Save Images-----//
if(WF_Visualization)
ImageView(image=imgNuc, item=imgNuc, label="imgNuc")
ImageView(image=imgNeu, item=imgNeu, label="imgNeu")
ImageView(image=imgFrangi, item=imgFrangi, label="imgFrangi")
CarryObjects(Stencil=nuclei.outerRegion, Data="Blue", image=imgNeu_Result_Mask | imgNeu_Result_Mask = image)
ImageView(image=imgNeu_Result_Mask, item=imgNeu_Result_Mask, label="imgNeu_Mask", title="ImgNeu_Mask: Red=Threshold, Yellow=Frangi, Blue=Nuclear Border")
ImageView(image=imgNuc_Result, item=imgNuc_Result, label="imgNuc_Result", title="ImgNuc_Result: Red=CutOff Area, Yellow=CutOff Roundness, Magenta=CutoOff Mask, Green=Neuronal")
ImageView(image=imgNeu, item=neurites.border, label="imgNeu_Neurites")
If(IN_clusteranalysis)
ImageView(image=imgNuc, item=clustersNuclei.border, label="imgNuc_Cluster")
ImageView(image=imgNuc, item=clustersNuclei_Neuronal.border, label="imgNuc_nuclei_neuronal_cluster")
ImageView(image=imgNuc, item=clustersNuclei_NonNeuronal.border, label="imgNuc_nuclei_nonneuronal_cluster")
End()
ImageView(image=imgNeu, item=skeleton.skeleton, label="imgNeu_Skeleton")
If(IN_SpotAnalysis1)
ImageView(image=imgNeu, item=neurites.searchRegion, label="imgNeu_SearchRegion")
ImageView(image=imgSpot1, item=imgSpot1, label="imgSpot1")
ImageView(image=imgSpot1, item=spot1.body, label="imgSpot1_Spot1")
End()
if(IN_SpotAnalysis2)
ImageView(image=imgSpot2, item=imgSpot2, label="imgSpot2")
ImageView(image=imgSpot2, item=spot2.body, label= "imgSpot2_Spot2")
End()
End()
Folder1="/" & Columbus::ValidFilenamePart(WellResults._RAW_PlateName).filename;
Folder2="/" & Columbus::ValidFilenamePart(WellResults._RAW_MeasurementDate).filename;
Folder3="/resultImages";
if(WF_SaveResultImages)
image_blank=sourcedata.sourceimage[0]*0;
if(length(IN_FolderResultOutput)>0)
set(filename="")
try()
if(FileInfo(IN_FolderResultOutput).FileInfo.exists)
if(!FileInfo(IN_FolderResultOutput & Folder1).FileInfo.exists)
MKDir(IN_FolderResultOutput & Folder1)
end()
if(!FileInfo(IN_FolderResultOutput & Folder1 & Folder2).FileInfo.exists)
MKDir(IN_FolderResultOutput & Folder1 & Folder2)
end()
if(!FileInfo(IN_FolderResultOutput & Folder3).FileInfo.exists)
MKDir(IN_FolderResultOutput & Folder1 & Folder2 & Folder3)
end()
sprintf(filename_base,"r%.2dc%.2df%.2d_",WellResults._RAW_WellRow,WellResults._RAW_WellColumn,FieldTable.Field[_field])
set(filename=filename_base & "A_Nuc")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=imgNuc)
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "A_Nuc_All")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(nuclei.@count,nuclei.body.@mask.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
if(IN_ClusterAnalysis)
set(filename=filename_base & "A_Nuc_Clusters")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(clustersNuclei.@count,clustersNuclei.body.@mask.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
End()
set(filename=filename_base & "A_Nuc_Neuronal")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(nuclei_neuronal.@count,nuclei_neuronal.body.@mask.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "B_Neu")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(neurites.@count,imgNeu,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "B_Neu_Detected")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(neurites.@count,neurites.body.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "B_Neu_Skeleton")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(neurites.@count,skeleton.skeleton.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
If(IN_SpotAnalysis1)
set(filename=filename_base & "B_Neu_Search")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(neurites.@count,neurites.searchRegion.@array,image_blank))
writeimage(fileName,imageformat="png", image=visualimage)
Delete(visualImage)
set(image_blank=imgSpot1*0.0)
sprintf(filename_base,"r%.2dc%.2df%.2d_",WellResults._RAW_WellRow,WellResults._RAW_WellColumn,FieldTable.Field[_field])
set(filename=filename_base & "C_Spot1")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(Spot1.@count,imgSpot1,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "C_Spot1_Det")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(Spot1.@count,Spot1.body.@mask.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
End()
If(IN_SpotAnalysis2)
set(filename=filename_base & "D_Spot2")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(spot2.@count,imgSpot2,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
set(filename=filename_base & "D_Spot2_Det")
set(filename=IN_FolderResultOutput & Folder1 & Folder2 & Folder3 & "/" & Columbus::ValidFilenamePart(filename).filename)
set(filename=iif(at(".png",filename),filename,filename & ".png"))
set(visualimage=iif(spot2.@count,spot2.body.@mask.@array,image_blank))
writeimage(fileName,imageformat="png",image=visualimage)
Delete(visualImage)
End()
end()
Catch_Error()
dbg::Log(5,"EVT","FieldProcessing","Collect Outputs - Error in writing " & filename)
end()
end()
end()
//-----Store FieldData-----//
if(WF_SaveResults)
FieldTable.FieldResults[_Field]=eval("FieldTable.FieldResults[_field]",cnt());
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_Intensity=textureImgNuc.int.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_IntensityStddev=textureImgNuc.int_stddev.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_Contrast=textureImgNuc.HaralickContrast.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_Correlation=textureImgNuc.HaralickCorrelation.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_Homogeneity=textureImgNuc.HaralickHomogeneity.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNuclei_SumVariance=textureImgNuc.HaralickSumVariance.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_Intensity=textureImgNeu.int.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_IntensityStddev=textureImgNeu.int_stddev.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_Contrast=textureImgNeu.HaralickContrast.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_Correlation=textureImgNeu.HaralickCorrelation.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_Homogeneity=textureImgNeu.HaralickHomogeneity.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelNeurite_SumVariance=textureImgNeu.HaralickSumVariance.@mean;
If(IN_SpotAnalysis1)
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_Intensity=textureImgSpot1.int.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_IntensityStddev=textureImgSpot1.int_stddev.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_Contrast=textureImgSpot1.HaralickContrast.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_Correlation=textureImgSpot1.HaralickCorrelation.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_Homogeneity=textureImgSpot1.HaralickHomogeneity.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot1_SumVariance=textureImgSpot1.HaralickSumVariance.@mean;
End()
If(IN_SpotAnalysis2)
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_Intensity=textureImgSpot2.int.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_IntensityStddev=textureImgSpot2.int_stddev.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_Contrast=textureImgSpot2.HaralickContrast.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_Correlation=textureImgSpot2.HaralickCorrelation.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_Homogeneity=textureImgSpot2.HaralickHomogeneity.@mean;
FieldTable.FieldResults[_Field]._MEAN_ChannelSpot2_SumVariance=textureImgSpot2.HaralickSumVariance.@mean;
End()
FieldTable.FieldResults[_Field]._MEAN_Neurite_Area=neurites.area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Neurite_AreaSearch=neurites.searchRegion.Area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Int=neurites.intensityNeu.@mean;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Int_sd=neurites.intensityNeu_stddev.@mean;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Length=skeleton.area.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Width_Mean=skeleton.dendrite_meanwidth.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Width_Max=skeleton.dendrite_maxwidth.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Neurite_Nodes=skeleton.skeleton_node_count.@mean;
FieldTable.FieldResults[_Field]._MEAN_Neurite_AreaPerNucleus=neurites.area.@mean*areaSizeCalibration/nuclei_neuronal.@count;
FieldTable.FieldResults[_Field]._MEAN_Neurite_LengthPerNucleus=skeleton.area.@mean*distanceSizeCalibration/nuclei_neuronal.@count;
if(at("X1",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntX1=Neurites.IntensityX1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Neurite_IntX1=Neurites.IntensityX1.@stddev;
End()
If(at("X2",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntX2=Neurites.IntensityX2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Neurite_IntX2=Neurites.IntensityX2.@stddev;
End()
If(at("X3",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntX3=Neurites.IntensityX3.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Neurite_IntX3=Neurites.IntensityX3.@stddev;
End()
If(at("X4",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntX4=Neurites.IntensityX4.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Neurite_IntX4=Neurites.IntensityX4.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Count=nuclei.@count;
if(IN_ClusterAnalysis)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_MinDistance=nuclei.NeighbourDistance.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_MinDistance=nuclei.NeighbourDistance.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NumberInCluster=nuclei.NumberOfObjectsInCluster.@mean;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NumberInCluster=nuclei.NumberOfObjectsInCluster.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Area=nuclei.area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Area=nuclei.area.@stddev*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Perimeter=nuclei.border_area.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Perimeter=nuclei.border_area.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Roundness=nuclei.Compactness.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Roundness=nuclei.Compactness.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Int=nuclei.IntenMean.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Int=nuclei.IntenMean.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Int_relDev=nuclei.IntenRelDev.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Int_relDev=nuclei.IntenRelDev.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntNeu=nuclei.IntensityNeu.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntNeu=nuclei.IntensityNeu.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_SERSpot=nuclei.SERSpot.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_SERSpot=nuclei.SERSpot.@stddev;
if(IN_SpotAnalysis1)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntSpot1=nuclei.IntensitySpot1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntSpot1=nuclei.IntensitySpot1.@stddev;
End()
if(IN_SpotAnalysis2)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntSpot2=nuclei.IntensitySpot2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntSpot2=nuclei.IntensitySpot2.@stddev;
End()
If(at("X1",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntX1=nuclei.IntensityX1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntX1=nuclei.IntensityX1.@stddev;
End()
If(at("X2",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntX2=nuclei.IntensityX2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntX2=nuclei.IntensityX2.@stddev;
End()
If(at("X3",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntX3=nuclei.IntensityX3.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntX3=nuclei.IntensityX3.@stddev;
End()
If(at("X4",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_IntX4=nuclei.IntensityX4.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_IntX4=nuclei.IntensityX4.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Count=nuclei_neuronal.@count;
if(IN_ClusterAnalysis)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_MinDistance=nuclei_neuronal.NeighbourDistance.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_MinDistance=nuclei_neuronal.NeighbourDistance.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_NumberInCluster=nuclei_neuronal.NumberOfObjectsInCluster.@mean;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_NumberInCluster=nuclei_neuronal.NumberOfObjectsInCluster.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Ratio=nuclei_neuronal.@count/nuclei.@count;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Area=nuclei_neuronal.area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_Area=nuclei_neuronal.area.@stddev*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Perimeter=nuclei_neuronal.border_area.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_Perimeter=nuclei_neuronal.border_area.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Roundness=nuclei_neuronal.Compactness.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_Roundness=nuclei_neuronal.Compactness.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Int=nuclei_neuronal.IntenMean.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_Int=nuclei_neuronal.IntenMean.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_Int_relDev=nuclei_neuronal.IntenRelDev.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_Int_relDev=nuclei_neuronal.IntenRelDev.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntNeu=nuclei_Neuronal.IntensityNeu.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntNeu=nuclei_Neuronal.IntensityNeu.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_SERSpot=nuclei_neuronal.SERSpot.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_SERSpot=nuclei_neuronal.SERSpot.@stddev;
if(IN_SpotAnalysis1)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntSpot1=nuclei_Neuronal.IntensitySpot1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntSpot1=nuclei_Neuronal.IntensitySpot1.@stddev;
End()
if(IN_SpotAnalysis2)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntSpot2=nuclei_Neuronal.IntensitySpot2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntSpot2=nuclei_Neuronal.IntensitySpot2.@stddev;
End()
If(at("X1",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntX1=nuclei_Neuronal.IntensityX1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntX1=nuclei_Neuronal.IntensityX1.@stddev;
End()
If(at("X2",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntX2=nuclei_Neuronal.IntensityX2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntX2=nuclei_Neuronal.IntensityX2.@stddev;
End()
If(at("X3",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntX3=nuclei_Neuronal.IntensityX3.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntX3=nuclei_Neuronal.IntensityX3.@stddev;
End()
If(at("X4",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_Neuronal_IntX4=nuclei_Neuronal.IntensityX4.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_Neuronal_IntX4=nuclei_Neuronal.IntensityX4.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Count=nuclei_NonNeuronal.@count;
if(IN_ClusterAnalysis)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_MinDistance=nuclei_NonNeuronal.NeighbourDistance.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_MinDistance=nuclei_NonNeuronal.NeighbourDistance.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_NumberInCluster=nuclei_NonNeuronal.NumberOfObjectsInCluster.@mean;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_NumberInCluster=nuclei_NonNeuronal.NumberOfObjectsInCluster.@stddev;
End()
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Ratio=nuclei_NonNeuronal.@count/nuclei.@count;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Area=nuclei_NonNeuronal.area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_Area=nuclei_NonNeuronal.area.@stddev*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Perimeter=nuclei_NonNeuronal.border_area.@mean*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_Perimeter=nuclei_NonNeuronal.border_area.@stddev*distanceSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Roundness=nuclei_NonNeuronal.Compactness.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_Roundness=nuclei_NonNeuronal.Compactness.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Int=nuclei_NonNeuronal.IntenMean.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_Int=nuclei_NonNeuronal.IntenMean.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_Int_relDev=nuclei_NonNeuronal.IntenRelDev.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_Int_relDev=nuclei_NonNeuronal.IntenRelDev.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntNeu=nuclei_NonNeuronal.IntensityNeu.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntNeu=nuclei_NonNeuronal.IntensityNeu.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_SERSpot=nuclei_NonNeuronal.SERSpot.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_SERSpot=nuclei_NonNeuronal.SERSpot.@stddev;
if(IN_SpotAnalysis1)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntSpot1=nuclei_NonNeuronal.IntensitySpot1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntSpot1=nuclei_NonNeuronal.IntensitySpot1.@stddev;
End()
if(IN_SpotAnalysis2)
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntSpot2=nuclei_NonNeuronal.IntensitySpot2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntSpot2=nuclei_NonNeuronal.IntensitySpot2.@stddev;
End()
If(at("X1",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntX1=nuclei_NonNeuronal.IntensityX1.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntX1=nuclei_NonNeuronal.IntensityX1.@stddev;
End()
If(at("X2",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntX2=nuclei_NonNeuronal.IntensityX2.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntX2=nuclei_NonNeuronal.IntensityX2.@stddev;
End()
If(at("X3",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntX3=nuclei_NonNeuronal.IntensityX3.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntX3=nuclei_NonNeuronal.IntensityX3.@stddev;
End()
If(at("X4",channelNames))
FieldTable.FieldResults[_Field]._MEAN_Nuclei_NonNeuronal_IntX4=nuclei_NonNeuronal.IntensityX4.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Nuclei_NonNeuronal_IntX4=nuclei_NonNeuronal.IntensityX4.@stddev;
End()
If(WF_Assay=="Connectivity")
If(IN_SpotAnalysis1)
FieldTable.FieldResults[_Field]._MEAN_Spot1_Count=spot1.@count;
FieldTable.FieldResults[_Field]._MEAN_Spot1_Int=spot1.Intensity.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Spot1_Int=spot1.Intensity.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Spot1_Area=spot1.Area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Spot1_DensityArea=spot1.@count/(neurites.searchRegion.Area.@mean*areaSizeCalibration);
FieldTable.FieldResults[_Field]._MEAN_Spot1_DensityLength=spot1.@count/(skeleton.area.@mean*distanceSizeCalibration);
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntSpot1=neurites.intensitySpot1.@mean;
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntBgSpot1=neurites.intensityBgSpot1;
FieldTable.FieldResults[_Field]._MEAN_Spot1_SBR=spot1.Intensity.@mean/neurites.intensityBgSpot1;
End()
if(IN_SpotAnalysis2)
FieldTable.FieldResults[_Field]._MEAN_Spot2_Count=spot2.@count;
FieldTable.FieldResults[_Field]._MEAN_Spot2_Int=spot2.Intensity.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Spot2_Int=spot2.Intensity.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Spot2_Area=spot2.Area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Spot2_DensityArea=spot2.@count/(neurites.searchRegion.Area.@mean*areaSizeCalibration);
FieldTable.FieldResults[_Field]._MEAN_Spot2_DensityLength=spot2.@count/(skeleton.area.@mean*distanceSizeCalibration);
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntSpot2=neurites.intensitySpot2.@mean;
FieldTable.FieldResults[_Field]._MEAN_Neurite_IntBgSpot2=neurites.intensityBgSpot2;
FieldTable.FieldResults[_Field]._MEAN_Spot2_SBR=spot2.Intensity.@mean/neurites.intensityBgSpot2;
FieldTable.FieldResults[_Field]._MEAN_Spot1_Overlap=spot1.overlap.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Spot1_Overlap=spot1.overlap.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Spot2_Overlap=spot2.overlap.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Spot2_Overlap=spot2.overlap.@stddev;
FieldTable.FieldResults[_Field]._MEAN_Spot1_PercWithOverlap=spot1PercWithOverlap;
FieldTable.FieldResults[_Field]._MEAN_Spot2_PercWithOverlap=spot2PercWithOverlap;
FieldTable.FieldResults[_Field]._MEAN_Synapses_Count=synapses.@count;
FieldTable.FieldResults[_Field]._MEAN_Synapses_Density=synapses.@count/(neurites.searchRegion.Area.@mean*areaSizeCalibration);
FieldTable.FieldResults[_Field]._MEAN_Synapses_Area=synapses.area.@mean*areaSizeCalibration;
FieldTable.FieldResults[_Field]._STDDEV_Synapses_Area=synapses.area.@stddev*areaSizeCalibration;
FieldTable.FieldResults[_Field]._MEAN_Synapses_IntRatio=synapses.IntRatio.@mean;
FieldTable.FieldResults[_Field]._STDDEV_Synapses_IntRatio=synapses.IntRatio.@stddev;
FieldTable.FieldResults[_Field]._MEAN_PearsonCorr=pearsonCorr;
FieldTable.FieldResults[_Field]._MEAN_SimilarityIndex=similarityIndex;
//FieldTable.FieldResults[_Field]._MEAN_RWC1=RWC1;
//FieldTable.FieldResults[_Field]._MEAN_RWC2=RWC2;
End()
End()
//-----Store WellData-----//
collectWellResults(variable="_MEAN_ChannelNuclei_Intensity",vec=textureImgNuc.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_Intensity",vec=textureImgNuc.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNuclei_IntensityStddev",vec=textureImgNuc.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_IntensityStddev",vec=textureImgNuc.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNuclei_Contrast",vec=textureImgNuc.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_Contrast",vec=textureImgNuc.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNuclei_Correlation",vec=textureImgNuc.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_Correlation",vec=textureImgNuc.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNuclei_Homogeneity",vec=textureImgNuc.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_Homogeneity",vec=textureImgNuc.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNuclei_SumVariance",vec=textureImgNuc.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNuclei_SumVariance",vec=textureImgNuc.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNeurite_Intensity",vec=textureImgNeu.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_Intensity",vec=textureImgNeu.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_channelNeurite_IntensityStddev",vec=textureImgNeu.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_IntensityStddev",vec=textureImgNeu.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNeurite_Contrast",vec=textureImgNeu.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_Contrast",vec=textureImgNeu.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNeurite_Correlation",vec=textureImgNeu.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_Correlation",vec=textureImgNeu.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNeurite_Homogeneity",vec=textureImgNeu.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_Homogeneity",vec=textureImgNeu.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelNeurite_SumVariance",vec=textureImgNeu.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelNeurite_SumVariance",vec=textureImgNeu.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
if(IN_SpotAnalysis1)
collectWellResults(variable="_MEAN_ChannelSpot1_Intensity",vec=textureImgSpot1.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_Intensity",vec=textureImgSpot1.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot1_IntensityStddev",vec=textureImgSpot1.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_IntensityStddev",vec=textureImgSpot1.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot1_Contrast",vec=textureImgSpot1.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_Contrast",vec=textureImgSpot1.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot1_Correlation",vec=textureImgSpot1.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_Correlation",vec=textureImgSpot1.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot1_Homogeneity",vec=textureImgSpot1.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_Homogeneity",vec=textureImgSpot1.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot1_SumVariance",vec=textureImgSpot1.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot1_SumVariance",vec=textureImgSpot1.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
End()
If(IN_SpotAnalysis2)
collectWellResults(variable="_MEAN_ChannelSpot2_Intensity",vec=textureImgSpot2.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_Intensity",vec=textureImgSpot2.int.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot2_IntensityStddev",vec=textureImgSpot2.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_IntensityStddev",vec=textureImgSpot2.int_stddev.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot2_Contrast",vec=textureImgSpot2.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_Contrast",vec=textureImgSpot2.HaralickContrast.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot2_Correlation",vec=textureImgSpot2.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_Correlation",vec=textureImgSpot2.HaralickCorrelation.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot2_Homogeneity",vec=textureImgSpot2.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_Homogeneity",vec=textureImgSpot2.HaralickHomogeneity.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_MEAN_ChannelSpot2_SumVariance",vec=textureImgSpot2.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
collectWellResults(variable="_STDDEV_ChannelSpot2_SumVariance",vec=textureImgSpot2.HaralickSumVariance.@mean,wellResults=wellResults | wellResults)
End()