7. ABCD Extraction - Part 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABCD Extraction - Part 2\n",
    "\n",
    "### This file contains the extraction of ABCD features. The steps performed are:\n",
    "\n",
    "1. Pass threshold image to asymmetry, border and diameter functions and result image to color function\n",
    "2. Find the total number of affected pixels (A), unaffected pixels, and the difference between unaffected and affected pixels (a).\n",
    "3. Computer asymmetry through the formula \n",
    "4. Perform edge detection and return matrix with value set to one where pixel border exists \n",
    "5. Calculate the inverse of shape factor \n",
    "6. Compute the border using the formula \n",
    "7. Compute standard deviation and mean for pixel values \n",
    "8. Compute the color.\n",
    "9. Compute the diameter using the formula\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import scipy\n",
    "from scipy import misc\n",
    "from scipy import ndimage\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import imageio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the different functions needed for the extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sobel(array):\n",
    "        Gdash=array\n",
    "        shape=array.shape\n",
    "        gx=np.array([[-2,-1,0,1,2] for i in range(0,5)])\n",
    "        gy=np.array([np.repeat(i,5) for i in range (-2,3)])\n",
    "        gf=np.array([[np.exp(-(gx[i,j]*gx[i,j]+gy[i,j]*gy[i,j])/3) for j in range(0,5)] for i in range(0,5)])\n",
    "        smooth=np.array([([np.sum(gf*array[i:i+5,j:j+5])/25 for j in range(0,shape[1]-4)]) for i in range(0,shape[0]-4)])\n",
    "        for i in range(2,shape[0]-2):\n",
    "            for j in range(2,shape[1]-2):\n",
    "                 Gdash[i,j]=smooth[i-2,j-2]\n",
    "        G=Gdash\n",
    "        sobely=np.array([[3,0,-3],[10,0,-10],[3,0,-3]])\n",
    "        sobelx=np.array([[3,10,3],[0,0,0],[-3,-10,-3]])\n",
    "        Gx=np.array([([np.sum(sobelx*array[i:i+3,j:j+3])/9 for j in range(0,shape[1]-2)]) for i in range(0,shape[0]-2)])\n",
    "        Gy=np.array([([np.sum(sobely*array[i:i+3,j:j+3])/9 for j in range(0,shape[1]-2)]) for i in range(0,shape[0]-2)])                \n",
    "        Gx=[[Gx[i,j]*Gx[i,j] for j in range(0,shape[1]-2)] for i in range(0,shape[0]-2)]\n",
    "        Gy=[[Gy[i,j]*Gy[i,j] for j in range(0,shape[1]-2)] for i in range(0,shape[0]-2)]\n",
    "        tempG=np.sqrt(np.add(Gx,Gy))\n",
    "        for i in range(1,shape[0]-1):\n",
    "            for j in range(1,shape[1]-1):\n",
    "                 G[i,j]=tempG[i-1,j-1]\n",
    "        G.astype(int)      \n",
    "        return G\n",
    "\n",
    "def Area(img):\n",
    "    naffect=0\n",
    "    affect=0\n",
    "    for rownum in range(len(img)):\n",
    "        for colnum in range(len(img[rownum])):\n",
    "            if img[rownum,colnum] == 0 :\n",
    "                naffect=naffect+1\n",
    "            else:\n",
    "                affect=affect+1\n",
    "    a=naffect-affect\n",
    "    A=affect\n",
    "    return a,A\n",
    "\n",
    "def ColourCalc(hsvimg):\n",
    "    mean=0\n",
    "    SD=0\n",
    "    for row in range(len(hsvimg)):\n",
    "        for col in range(len(hsvimg[row])):\n",
    "            mean=mean+hsvimg[row,col,0]\n",
    "    for row in range(len(hsvimg)):\n",
    "        for col in range(len(hsvimg[row])):\n",
    "            SD=SD+(hsvimg[row,col,0]-mean)*(hsvimg[row,col,0]-mean)\n",
    "    width,height,val=hsvimg.shape\n",
    "    n=width*height\n",
    "    SD=math.sqrt(SD)/(n-1)\n",
    "    return SD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the ABCD values of a benign sample image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "eHTYPXiJ6RTB",
    "outputId": "de0a3a1c-7fb3-48a0-d1b1-a06ec167b2d3"
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asymmetry\n",
      "55.86332022981554\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Border\n",
      "0.7100167758412478\n",
      "Diameter\n",
      "0.5869234759887073\n",
      "Colour\n",
      "1.3350674216473404\n"
     ]
    }
   ],
   "source": [
    "if __name__== \"__main__\":\n",
    "\n",
    "    img=imageio.imread('C:/Users/Kannan-PC/Desktop/Projects/melanoma/Final Year Project/Result.jpg')\n",
    "    img2=imageio.imread('C:/Users/Kannan-PC/Desktop/Projects/melanoma/Final Year Project/threshold.jpg')\n",
    "    plt.title('Processed image')\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    dilimg=ndimage.binary_dilation(img2)\n",
    "    for x in range(25):\n",
    "        dilimg=ndimage.binary_dilation(dilimg)\n",
    "    erimg=ndimage.binary_erosion(dilimg)\n",
    "    for x in range(25):\n",
    "        erimg=ndimage.binary_erosion(erimg)\n",
    "    plt.title('Closed image')\n",
    "    plt.imshow(erimg,cmap=cm.Greys_r)\n",
    "    plt.show()\n",
    "    a,A=Area(erimg)\n",
    "\n",
    "    Asymmetry=((a/A)*100)/10\n",
    "    if Asymmetry<0 :\n",
    "        Asymmetry=Asymmetry*(-1)\n",
    "    print('Asymmetry')\n",
    "    print(Asymmetry)\n",
    "\n",
    "\n",
    "    gigi=sobel(erimg)\n",
    "    plt.imshow(gigi,cmap=cm.Greys_r)\n",
    "    plt.show()\n",
    "    pcount=0\n",
    "    for row in range(len(gigi)):\n",
    "        for col in range(len(gigi[row])):\n",
    "            if gigi[row,col] == 1:\n",
    "                pcount=pcount+1\n",
    "    P=pcount\n",
    "    Border=((P*P)/(4*3.14*A))/10\n",
    "    print('Border')\n",
    "    print(Border)\n",
    "\n",
    "    n=(4*A)/P\n",
    "    Diameter=(math.sqrt(n))/10\n",
    "    print('Diameter')\n",
    "    print(Diameter)\n",
    "\n",
    "    hsvimg=matplotlib.colors.rgb_to_hsv(img)\n",
    "\n",
    "    Colour=(ColourCalc(hsvimg))/10\t\n",
    "    print('Colour')\n",
    "    print(Colour)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "ABCDE Rule Melanomz.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}