Module1-ImageHandling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data load - display and normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pandas as pd\n",
    "import csv\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn import preprocessing\n",
    "import nibabel as nib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the images\n",
    "We have 3 types of images available:\n",
    "\n",
    "* T1w pre-contrast\n",
    "* FLAIR\n",
    "* T1w post-contrast\n",
    "\n",
    "The goal is to create a 4D image that contains all four 3D volumes we will use in our example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\Medical-Image-Tutorials\\MachineLearningForMedicalImages-master\\notebooks\n"
     ]
    }
   ],
   "source": [
    "CurrentDir= os.getcwd()\n",
    "\n",
    "# Print current directory\n",
    "print (CurrentDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the file paths. The images are contained in a subfolder called Data. \n",
    "# TO DO: make this a function\n",
    "PostName =  os.path.abspath(os.path.join(os.path.abspath(os.path.join(CurrentDir, os.pardir)), \"Data\",  'POST.nii.gz')  )\n",
    "PreName =  os.path.abspath(os.path.join(os.path.abspath(os.path.join(CurrentDir, os.pardir)), \"Data\", 'PRE.nii.gz') )\n",
    "FLAIRName =  os.path.abspath(os.path.join(os.path.abspath(os.path.join(CurrentDir, os.pardir)), \"Data\", 'FLAIR.nii.gz') )\n",
    "GroundTruth=  os.path.abspath(os.path.join(os.path.abspath(os.path.join(CurrentDir, os.pardir)), \"Data\", 'GroundTruth.nii.gz') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Medical-Image-Tutorials\\\\MachineLearningForMedicalImages-master\\\\Data\\\\POST.nii.gz'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PostName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Medical-Image-Tutorials\\\\MachineLearningForMedicalImages-master\\\\Data\\\\PRE.nii.gz'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PreName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\Medical-Image-Tutorials\\\\MachineLearningForMedicalImages-master\\\\Data\\\\FLAIR.nii.gz'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FLAIRName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: 240, 240, 155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\deniz\\AppData\\Local\\Temp/ipykernel_17528/4074791610.py:4: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  Pre=Pre.get_data()\n"
     ]
    }
   ],
   "source": [
    "# read Pre in--we assume that all images are same x,y dims\n",
    "Pre = nib.load(PreName)\n",
    "# Pre is a class containing the image data among other information \n",
    "Pre=Pre.get_data()\n",
    "xdim = np.shape(Pre)[0]\n",
    "ydim = np.shape(Pre)[1]\n",
    "zdim = np.shape(Pre)[2]\n",
    "# Printing the dimensions of an image \n",
    "print(f'Dimensions: {xdim}, {ydim}, {zdim}')\n",
    "# Normalize to mean\n",
    "Pre=Pre/np.mean(Pre[np.nonzero(Pre)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post & Flair images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\deniz\\AppData\\Local\\Temp/ipykernel_17528/3388422037.py:3: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  Post=Post.get_data()\n",
      "C:\\Users\\deniz\\AppData\\Local\\Temp/ipykernel_17528/3388422037.py:7: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  Flair=Flair.get_data()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Loaded\n"
     ]
    }
   ],
   "source": [
    "# Post\n",
    "Post = nib.load(PostName)\n",
    "Post=Post.get_data()\n",
    "# Normalize to mean\n",
    "Post=Post/np.mean(Post[np.nonzero(Post)])\n",
    "Flair = nib.load(FLAIRName)\n",
    "Flair=Flair.get_data()\n",
    "# Normalize FLAIR \n",
    "Flair=Flair/np.mean(Flair[np.nonzero(Flair)])\n",
    "print (\"Data Loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Flair"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training set\n",
    "\n",
    "We assume the following labels:\n",
    "\n",
    "* Enhancing Tumor = 4\n",
    "\n",
    "* Edema = 2\n",
    "\n",
    "* WM and CSF and GM=1\n",
    "\n",
    "* Background (air) = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\deniz\\AppData\\Local\\Temp/ipykernel_17528/2555537840.py:3: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  GroundTruth=GroundTrutha.get_data()\n"
     ]
    }
   ],
   "source": [
    "#  Load Ground Truth\n",
    "GroundTrutha = nib.load(GroundTruth)\n",
    "GroundTruth=GroundTrutha.get_data()\n",
    "print (\"Data Loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def display_overlay(Image1, Image2):\n",
    "    \"\"\"\n",
    "    Function: Overlays Image2 over Image1\n",
    "    Image 1: 2D image\n",
    "    Image 2: 2D Image\n",
    "\n",
    "    Requires numpy, matplotlib\n",
    "    \"\"\"\n",
    "    Image1=np.rot90(Image1,3)\n",
    "    Image2=np.rot90(Image2,3)\n",
    "    Image2 = np.ma.masked_where(Image2 == 0, Image2)\n",
    "    plt.imshow(Image1, cmap=plt.cm.gray)\n",
    "    plt.imshow(Image2, cmap=plt.cm.brg, alpha=.7, vmin=.7, vmax=5, interpolation='nearest')\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "f, (ax1,ax2,ax3,ax4)=plt.subplots(1,4)\n",
    "ax1.imshow(np.rot90(Post[:, :, 55,],3), cmap=plt.cm.gray)\n",
    "ax1.axis('off')\n",
    "ax2.imshow(np.rot90(Flair[:, :, 55,],3), cmap=plt.cm.gray)\n",
    "ax2.axis('off')\n",
    "ax3.imshow(np.rot90(Pre[:, :, 55,],3), cmap=plt.cm.gray)\n",
    "ax3.axis('off')\n",
    "ax4.imshow(np.rot90(GroundTruth[:, :, 55,],3), cmap=plt.cm.gray)\n",
    "ax4.axis('off')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "display_overlay(Post[:, :, 55,], GroundTruth[:,:,55]==4)  \n",
    "display_overlay(Flair[:, :, 55,], GroundTruth[:,:,55]==2)  \n",
    "display_overlay(Pre[:, :, 55,], GroundTruth[:,:,55]==1)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the data to a pandas dataframe and subsequently to a csv\n"
     ]
    }
   ],
   "source": [
    "# Create classes\n",
    "# Tissue =GM+CSG+WM\n",
    "ClassTissuePost=(Post[np.nonzero(GroundTruth==1)])\n",
    "ClassTissuePre=(Pre[np.nonzero(GroundTruth==1)])\n",
    "ClassTissueFlair=(Flair[np.nonzero(GroundTruth==1)])\n",
    "# Enhancing Tumor \n",
    "ClassTumorPost=(Post[np.nonzero(GroundTruth==4)])\n",
    "ClassTumorPre=(Pre[np.nonzero(GroundTruth==4)])\n",
    "ClassTumorFlair=(Flair[np.nonzero(GroundTruth==4)])\n",
    "# Edema \n",
    "ClassEdemaPost=(Post[np.nonzero(GroundTruth==2)])\n",
    "ClassEdemaPre=(Pre[np.nonzero(GroundTruth==2)])\n",
    "ClassEdemaFlair=(Flair[np.nonzero(GroundTruth==2)])\n",
    "# We only select 1000 points for demosntration purposes\n",
    "IND=np.random.randint(np.shape(ClassTumorPre)[0], size=5000)\n",
    "ClassTissuePost=ClassTissuePost[IND]\n",
    "ClassTissuePre=ClassTissuePre[IND]\n",
    "ClassTissueFlair=ClassTissueFlair[IND]\n",
    "ClassTumorPost=ClassTumorPost[IND]\n",
    "ClassTumorPre=ClassTumorPre[IND]\n",
    "ClassTumorFlair=ClassTumorFlair[IND]\n",
    "ClassEdemaPost=ClassEdemaPost[IND]\n",
    "ClassEdemaPre=ClassEdemaPre[IND]\n",
    "ClassEdemaFlair=ClassEdemaFlair[IND]\n",
    "print (\"Saving the data to a pandas dataframe and subsequently to a csv\")\n",
    "# Create a dictionary containing the classes\n",
    "datasetcomplete={\"ClassTissuePost\": ClassTissuePost, \"ClassTissuePre\": ClassTissuePre, \"ClassTissueFlair\": ClassTissueFlair, \"ClassTumorPost\": ClassTumorPost, \"ClassTumorPre\": ClassTumorPre, \"ClassTumorFlair\": ClassTumorFlair, \"ClassEdemaPost\": ClassEdemaPost, \"ClassEdemaPre\": ClassEdemaPre, \"ClassEdemaFlair\": ClassEdemaFlair}\n",
    "datapd=pd.DataFrame.from_dict(datasetcomplete,orient=\"index\")\n",
    "# print (datapd)\n",
    "datapd=datapd.transpose()\n",
    "# datapd=pd.DataFrame(dict([ (k,Series(v)) for k,v in datasetcomplete.iteritems() ]))\n",
    "datapd.to_csv(\"DataExample.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display Tumor vs NAWM\n",
    "IND=np.random.randint(1000, size=100)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(ClassTissuePost[IND,], ClassTissuePre[IND,], ClassTissueFlair[IND,])\n",
    "ax.scatter(ClassTumorPost[IND,], ClassTumorPre[IND,], ClassTumorFlair[IND,], c='r', marker='^')\n",
    "ax.set_xlabel('post')\n",
    "ax.set_ylabel('pret')\n",
    "ax.set_zlabel('FLAIR')\n",
    "plt.show()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(ClassTissuePost[IND,], ClassTissuePre[IND,])\n",
    "ax.scatter(ClassTumorPost[IND,], ClassTumorPre[IND,], c='r', marker='^')\n",
    "ax.set_xlabel('post')\n",
    "ax.set_ylabel('pret')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ClassTissuePost  ClassTissuePre  ClassTissueFlair  ClassTumorPost  \\\n",
      "count      5000.000000     5000.000000       5000.000000     5000.000000   \n",
      "mean          0.678505        0.735751          0.600106        1.608760   \n",
      "std           0.424010        0.405540          0.441649        0.325298   \n",
      "min           0.000000        0.002533          0.000000        0.420744   \n",
      "25%           0.198120        0.355251          0.162183        1.376983   \n",
      "50%           0.862615        0.816145          0.531986        1.597986   \n",
      "75%           1.027426        1.107585          1.053256        1.826394   \n",
      "max           2.011594        2.055752          1.874936        2.801992   \n",
      "\n",
      "       ClassTumorPre  ClassTumorFlair  ClassEdemaPost  ClassEdemaPre  \\\n",
      "count    5000.000000      5000.000000     5000.000000    5000.000000   \n",
      "mean        1.142995         1.402287        1.020012       1.099344   \n",
      "std         0.231390         0.381349        0.125137       0.080800   \n",
      "min         0.335215         0.077500        0.079645       0.470760   \n",
      "25%         0.993981         1.153676        0.930356       1.044556   \n",
      "50%         1.129484         1.400069        1.029414       1.102823   \n",
      "75%         1.238190         1.655365        1.101550       1.154156   \n",
      "max         2.266176         2.947998        1.725272       1.605886   \n",
      "\n",
      "       ClassEdemaFlair  \n",
      "count      5000.000000  \n",
      "mean          1.437874  \n",
      "std           0.212908  \n",
      "min           0.072902  \n",
      "25%           1.273248  \n",
      "50%           1.454359  \n",
      "75%           1.623724  \n",
      "max           1.861545  \n"
     ]
    }
   ],
   "source": [
    "# descriptions\n",
    "print(datapd.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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