Avinash Uppuluri

Hi bala,

Please try to explain further. From what I understand it looks like you want to know how to generate the E I S H for each image in your database and use the same parameters of the input image to find the closest ( euclidean ) image to it in the database.

You can generate the GLCM for each image using the in built matlab function graycomatrix() and then pass the generated GLCM to the function you downloaded here GLCM_featuresX()

Hope that helps,
Avinash

Kapil,

I would appreciate if you can leave a short comment on how you think this work can be improved so that I can make it more useful for yourself and others.

Thanks,
Avinash

Hi avinash, i think the formula for calculated TN (True Negative) is something wrong... I'm sorry if wrong...

function [out] = cad_glcm_features(glcm)

% VECTORIZED CODE: FASTER

size_glcm_1 = size(glcm,1);
size_glcm_2 = size(glcm,2);
size_glcm_3 = size(glcm,3);

% checked
out.autoc = zeros(1,size_glcm_3); % Autocorrelation: [2]
out.contr = zeros(1,size_glcm_3); % Contrast: matlab/[1,2]
out.corrm = zeros(1,size_glcm_3); % Correlation: matlab
out.corrp = zeros(1,size_glcm_3); % Correlation: [1,2]
out.cprom = zeros(1,size_glcm_3); % Cluster Prominence: [2]
out.cshad = zeros(1,size_glcm_3); % Cluster Shade: [2]
out.dissi = zeros(1,size_glcm_3); % Dissimilarity: [2]
out.energ = zeros(1,size_glcm_3); % Energy: matlab / [1,2]
out.entro = zeros(1,size_glcm_3); % Entropy: [2]
out.homom = zeros(1,size_glcm_3); % Homogeneity: matlab
out.homop = zeros(1,size_glcm_3); % Homogeneity: [2]
out.maxpr = zeros(1,size_glcm_3); % Maximum probability: [2]
out.sosvh = zeros(1,size_glcm_3); % Sum of sqaures: Variance [1]
out.savgh = zeros(1,size_glcm_3); % Sum average [1]
out.svarh = zeros(1,size_glcm_3); % Sum variance [1]
out.senth = zeros(1,size_glcm_3); % Sum entropy [1]
out.dvarh = zeros(1,size_glcm_3); % Difference variance [4]
out.denth = zeros(1,size_glcm_3); % Difference entropy [1]
out.inf1h = zeros(1,size_glcm_3); % Information measure of correlation1 [1]
out.inf2h = zeros(1,size_glcm_3); % Informaiton measure of correlation2 [1]
out.indnc = zeros(1,size_glcm_3); % Inverse difference normalized (INN) [3]
out.idmnc = zeros(1,size_glcm_3); % Inverse difference moment normalized [3]

% Indices
[i,j] = meshgrid(1:size_glcm_1,1:size_glcm_2);
idx1 = (i+j)-1;
idx2 = abs(i-j)+1;
ii = (1:(2*size_glcm_1-1))';
jj = (0:size_glcm_1-1)';

for k = 1:size_glcm_3 % number glcms
    % Normalize GLCM
    glcm_sum = sum(sum(glcm(:,:,k)));
    Pij = glcm(:,:,k)./glcm_sum; % Normalize each glcm
    glcm_mean = mean(Pij(:)); % compute mean after norm
    %
    p_x = squeeze(sum(Pij,2));
    p_y = squeeze(sum(Pij,1))';
    %
    u_x = sum(sum(i.*Pij));
    u_y = sum(sum(j.*Pij));
    %
    p_xplusy = zeros((2*size_glcm_1 - 1),1); %[1]
    p_xminusy = zeros((size_glcm_1),1); %[1]
    for aux = 1:max(idx1(:))
       p_xplusy(aux) = sum(Pij(idx1==aux));
    end
    for aux = 1:max(idx2(:))
       p_xminusy(aux) = sum(Pij(idx2==aux));
    end
    
    % Contrast
    out.contr(k) = sum(sum((abs(i-j).^2).*Pij));
    % Dissimilarity
    out.dissi(k) = sum(sum(abs(i-j).*Pij));
    % Energy
    out.energ(k) = sum(sum(Pij.^2));
    % Entropy
    out.entro(k) = -sum(sum(Pij.*log(Pij+eps)));
    % Homogeneity Matlab
    out.homom(k) = sum(sum(Pij./(1+abs(i-j))));
    % Homogeneity Paper
    out.homop(k) = sum(sum(Pij./(1+abs(i-j).^2)));
    % Sum of squares: Variance
    out.sosvh(k) = sum(sum(Pij.*((j-glcm_mean).^2)));
    % Inverse difference normalized
    out.indnc(k) = sum(sum(Pij./(1+(abs(i-j)./size_glcm_1))));
    % Inverse difference moment normalized
    out.idmnc(k) = sum(sum(Pij./(1+((i-j)./size_glcm_1).^2)));
    % Maximum probability
    out.maxpr(k) = max(Pij(:));
    % Sum average
    out.savgh(k) = sum((ii+1).*p_xplusy);
    % Sum entropy
    out.senth(k) = -sum(p_xplusy.*log(p_xplusy+eps));
    % Sum variance
    out.svarh(k) = sum((((ii+1) - out.senth(k)).^2).*p_xplusy);
    % Difference entropy
    out.denth(k) = -sum(p_xminusy.*log(p_xminusy+eps));
    % Difference variance
    out.dvarh(k) = sum((jj.^2).*p_xminusy);
    % Computes correlation
    hxy1 = -sum(sum(Pij.*log(p_x*p_y' + eps)));
    hxy2 = -sum(sum((p_x*p_y').*log(p_x*p_y' + eps)));
    hx = -sum(p_x.*log(p_x+eps));
    hy = -sum(p_y.*log(p_y+eps));
    hxy = out.entro(k);
    % Information measure of correlation 1
    out.inf1h(k) = (hxy-hxy1)/(max([hx,hy]));
    % Information measure of correlation 2
    out.inf2h(k) = (1-exp(-2*(hxy2-hxy)))^0.5;
    % Cluster Prominence
    out.cprom(k) = sum(sum(Pij.*((i+j-u_x-u_y).^4)));
    % Cluster Shade
    out.cshad(k) = sum(sum(Pij.*((i+j-u_x-u_y).^3)));
    %
    s_x = sum(sum(Pij.*((i-u_x).^2)))^0.5;
    s_y = sum(sum(Pij.*((j-u_y).^2)))^0.5;
    corp = sum(sum(Pij.*(i.*j)));
    corm = sum(sum(Pij.*(i-u_x).*(j-u_y)));
    % Autocorrelation
    out.autoc(k) = corp;
    % Correlation paper
    out.corrp(k) = (corp-u_x*u_y)/(s_x*s_y);
    % Correlation Matlab
    out.corrm(k) = corm/(s_x*s_y);
end