Colorectal Histology Image Classification

Classification of different types of colorectal cancer based on features Extracted by Texture Analysis.

Colorectal Histology Image Classification-

clc;
clear all;
close all;


A = imread('adipose.tif');clc;


%OFFSET AND DIRECTION
Displacementx = -1;
Displacementy = 1;


  
As = (A-min(A(:)))+1;

NumQuantLevels = max(As(:));
glcm = zeros([NumQuantLevels,NumQuantLevels]);

if ( Displacementx < 0 ) 
    sx=abs(Displacementx)+1;
    ex=size(As,1);
else
    sx=1;
    ex=size(As,1)-Displacementx;
end

if ( Displacementy < 0 ) 
    sy=abs(Displacementy)+1;
    ey=size(As,2);
else
    sy=1;
    ey =size(As,2)-Displacementy;
end
for i=sx:ex
    for j=sy:ey
        glcm(As(i,j),As(i+(1*Displacementx),j+(1*Displacementy)))=glcm(As(i,j),As(i+(1*Displacementx),j+(1*Displacementy)))+1;
    end
end



GLCMProb = glcm./sum(glcm(:));
[jj,ii]=meshgrid(1:size(glcm,1),1:size(glcm,2));
ij=ii-jj;

Con = sum(sum(GLCMProb.*(ij).^2));
Diss = sum(sum(GLCMProb.*abs(ij)));
Hom = sum(sum(GLCMProb./(1+(ij).^2)));
Asm = sum(sum(GLCMProb.^2));     
Meanx = sum(sum(GLCMProb.*ii)); 
Meany = sum(sum(GLCMProb.*jj));        

Energy = sqrt(Asm);
MaxGLCM = max(GLCMProb(:));
Entropy = sum(sum(-GLCMProb .* log10(GLCMProb+0.00001)));
GLCMMean = (Meanx+Meany)/2;

Varx = sum(sum(GLCMProb.*(ii-Meanx).^2));
Vary = sum(sum(GLCMProb.*(jj-Meany).^2));

GLCMVar = (Varx+Vary)/2;

GLCMCorrelation = sum(sum(GLCMProb.*(ii-Meanx).*(jj-Meany)/sqrt(Varx*Vary)));


CG=sprintf("Contrast Group:\nContrast:%f\nDissimilarity:%f\nHomogeneity:%f\n",Con,Diss,Hom);
OG=sprintf("Orderliness Group:\nAngular Second Moment :%f\nMax Probability:%f\nEntropy:%f\nEnergy:%f\n",Asm,MaxGLCM,Entropy,Energy);
SG=sprintf("Statistics Group:\nGLCM Mean:%f\nGLCM Variance:%f\nGLCM Correlation:%f\n",GLCMMean,GLCMVar,GLCMCorrelation);
display(CG);
display(OG);
display(SG);

%Classification

if (((50<GLCMMean)&&(GLCMMean<109))&&(GLCMVar<30))
    disp('Input image is of Empty class.')
elseif(((50<GLCMMean)&&(GLCMMean<109))&&((1500<GLCMVar)&&(GLCMVar<6000)))
    disp('Input image is of Lympho class.')
elseif(GLCMMean>179)
    disp('Input image is of Adipose class.')
elseif((110<GLCMMean)&&(GLCMMean<135))
    disp('Input image is of Tumor class.')
elseif (((130<GLCMMean)&&(GLCMMean<180))&&((1000<GLCMVar)&&(GLCMVar<1400)))
    disp('Input image is of stroma class.')
elseif (((130<GLCMMean)&&(GLCMMean<180))&&(GLCMVar>1401))
    disp('Input image is of Complex class.')
   

else 
    disp('Input image is of some other class.')
end

    

Visual Representation-

Here, the first 10 images of every tissue class in the dataset are shown. They represent the wide variation of illumination, stain intensity and tissue textures present in routine histopathological images. Images were extracted from 10 independent samples of colorectal cancer (CRC) primary tumours. (a) tumour epithelium, (b) simple stroma, (c) complex stroma (stroma that contains single tumour cells and/or single immune cells), (d) immune cell conglomerates, (e) debris and mucus, (f) mucosal glands, (g) adipose tissue, (h) background.

DataSet has been taken from-https://zenodo.org/record/53169#.W6HwwP4zbOQ

Method-

Feature Extraction by Texture Analysis and Image Classification Based on Features.

Step1:Color Transformation is Used.

Step2: Texture Analysis

1_GLCM method has been used .

2_Following Features has been calculated.

Contrast Group:

• Contrast (Con)
• Dissimilarity (Diss)
• Homogeneity (Hom)

Orderliness Group:

• Angular Second Moment (Asm)
• Maximum Probability (Max)
• Entropy (Entropy)

Statistics Group:

• GLCM Mean
• GLCM Variance
• GLCM Correlation

Step3: Classification based on Prominent features values & classified as per different types

Result- On the basis of feature extracted from texture analysis the Image has been Classified .