kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd  \n",
    "import importlib\n",
    "import os\n",
    "import numpy as np\n",
    "from skimage import measure\n",
    "from ipywidgets import interact\n",
    "from ipywidgets import interact_manual\n",
    "from IPython.display import display\n",
    "from ipywidgets import widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'ipynb.fs.full.dicomHandler' (/mnt/6884ACCB84AC9CD6/OneDrive - FEI/Aulas/Graduacao/8 - Semestre/CC8421 - TRABALHO FINAL DE CURSO II/image-segmentation/dicomHandler.ipynb)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipynb.fs.full.wspFFA as wspFirefly\n",
    "import ipynb.fs.full.wspCS as wspCuckooSearch\n",
    "import ipynb.fs.full.wspKH as wspKrillHerd\n",
    "import ipynb.fs.full.wspEHO as wspElephantHerding\n",
    "import ipynb.fs.full.wspABC as wspArtificialBeeColony\n",
    "\n",
    "import ipynb.fs.full.plotGraph as plotGraph\n",
    "import ipynb.fs.full.wspMultithreshold as wspMultithreshold\n",
    "import ipynb.fs.full.dicomHandler as dicomHandler\n",
    "\n",
    "importlib.reload(wspFirefly)\n",
    "importlib.reload(wspCuckooSearch)\n",
    "importlib.reload(wspKrillHerd)\n",
    "importlib.reload(wspElephantHerding)\n",
    "importlib.reload(wspArtificialBeeColony)\n",
    "\n",
    "importlib.reload(plotGraph)\n",
    "importlib.reload(wspMultithreshold)\n",
    "importlib.reload(dicomHandler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_dir = \"../Database/2EFVJVCN/\"\n",
    "file = \"I1680000\"\n",
    "bio_algorithms = 'FFA'\n",
    "dimension = 2\n",
    "q = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = folder_dir + file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicom_image, pixel_array = dicomHandler.read_dicom_image(image)\n",
    "\n",
    "transformed_pixel_array = dicomHandler.image_transformation(pixel_array)\n",
    "\n",
    "original_image = pixel_array.copy()\n",
    "    \n",
    "hu_image = dicomHandler.transform_to_hu(dicom_image, transformed_pixel_array)\n",
    "\n",
    "hist, bin_edges, best_thresholds, img_thres = wspMultithreshold.wspMultithreshold(hu_image, bio_algorithms, dimension, q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1024.0 1923.0\n"
     ]
    }
   ],
   "source": [
    "high_intensity = wspMultithreshold.get_high_intensity_pixels(img_thres)\n",
    "\n",
    "pixel_array = dicomHandler.transform_to_pixel_array(dicom_image, high_intensity)\n",
    "\n",
    "hard_tissue = wspMultithreshold.get_largests_regions(pixel_array, original_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dicomHandler.show_dicom_image(pixel_array)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('tcc_env')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "50c3a715107fe4dcdc7dcc1225e41dc08fefd31cb688eb0c1f730ec66093fb86"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}