my project work deals with classification of WBCs and counting of WBCs. here l am k-means clustering is used to segment the WBCs and extract some features using GLCM(mean,SD,correlation,entropy,energy....etc). after that i want to classify the WBCs into its five categories.for that purpose i decided to use the CNN.so i need a help to classify the WBC Using CNN.please help me through the code.
here is the program that i have done:
clc; % Clear command window.
clear; % Delete all variables.
close all; % Close all figure windows except those created by imtool.
imtool close all; % Close all figure windows created by imtool.
workspace; % Make sure the workspace panel is showing.
fontSize = 15;
[filename, pathname] = uigetfile({'*.*';'*.bmp';'*.jpg';'*.gif'}, 'Pick a Leaf Image File');
rgbImage = imread([pathname,filename]);
rgbImage = imresize(rgbImage,[256,256]);
%%image noise removal using median filter
% Get the dimensions of the image. numberOfColorBands should be = 3.
[rows columns numberOfColorBands] = size(rgbImage);
% Display the original color image.
%subplot(3, 4, 1);
%imshow(rgbImage);
%title('Original color Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'Position', get(0,'Screensize'));
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
% Generate a noisy image. This has salt and pepper noise independently on
% each color channel so the noise may be colored.
noisyRGB = imnoise(rgbImage,'salt & pepper', 0.05);
%%subplot(3, 4, 5);
%%imshow(noisyRGB);
%%title('Image with Salt and Pepper Noise', 'FontSize', fontSize);
% Extract the individual red, green, and blue color channels.
redChannel = noisyRGB(:, :, 1);
greenChannel = noisyRGB(:, :, 2);
blueChannel = noisyRGB(:, :, 3);
% Median Filter the channels:
redMF = medfilt2(redChannel, [3 3]);
greenMF = medfilt2(greenChannel, [3 3]);
blueMF = medfilt2(blueChannel, [3 3]);
% Find the noise in the red.
noiseImage = (redChannel == 0 | redChannel == 255);
% Get rid of the noise in the red by replacing with median.
noiseFreeRed = redChannel;
noiseFreeRed(noiseImage) = redMF(noiseImage);
% Find the noise in the green.
noiseImage = (greenChannel == 0 | greenChannel == 255);
% Get rid of the noise in the green by replacing with median.
noiseFreeGreen = greenChannel;
noiseFreeGreen(noiseImage) = greenMF(noiseImage);
% Find the noise in the blue.
noiseImage = (blueChannel == 0 | blueChannel == 255);
% Get rid of the noise in the blue by replacing with median.
noiseFreeBlue = blueChannel;
noiseFreeBlue(noiseImage) = blueMF(noiseImage);
% Reconstruct the noise free RGB image
rgbFixed = cat(3, noiseFreeRed, noiseFreeGreen, noiseFreeBlue);
%%subplot(3, 4, 9);
%%imshow(rgbFixed);
%%title('Restored Image', 'FontSize', fontSize);
% Enhance Contrast
rgbFixed = imadjust(rgbFixed,stretchlim(rgbFixed));
%image sharpening using Gaussian un-sharp mask
%create unsharp mask
H = padarray(2,[2,2])-fspecial('gaussian',[5 5],2);
% create a sharpened version of the image using that mask
sharpened = imfilter(rgbFixed,H);
%% L*a*b Color Space
%inputImLAB = rgb2lab(scale);
%subplot(1,2,2);
%imshow(inputImLAB);
cform = makecform('srgb2lab');
% Apply the colorform
lab_he = applycform(sharpened,cform);
% Extract a* and b* channels and reshape
ab = double(lab_he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
ab = reshape(ab,nrows*ncols,2);
nColors = 4;
[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean', ...
'Replicates',3);
%[cluster_idx cluster_center] = kmeans(ab,nColors,'distance','sqEuclidean','Replicates',3);
% Label every pixel in tha image using results from K means
pixel_labels = reshape(cluster_idx,nrows,ncols);
%figure,imshow(pixel_labels,[]), title('Image Labeled by Cluster Index');
% Create a blank cell array to store the results of clustering
segmented_images = cell(1,3);
% Create RGB label using pixel_labels
rgb_label = repmat(pixel_labels,[1,1,3]);
for k = 1:nColors
colors = rgbFixed;
colors(rgb_label ~= k) = 0;
segmented_images{k} = colors;
end
figure; subplot(4,1,1);imshow(segmented_images{1});
%title('Cluster 1');
subplot(4,1,2);imshow(segmented_images{2});
%title('Cluster 2');
subplot(4,1,3);imshow(segmented_images{3});
%title('Cluster 3');
subplot(4,1,4);imshow(segmented_images{4});
%title('Cluster 4')
set(gcf, 'Position', get(0,'Screensize'));
% Feature Extraction
x = inputdlg('Enter the cluster no. containing the ROI only:');
i = str2double(x);
% Extract the features from the segmented image
seg_img = segmented_images{i};
% Convert to grayscale if image is RGB
if ndims(seg_img) == 3
img = rgb2gray(seg_img);
end
%figure, imshow(img); title('Gray Scale Image');
%img = rgb2gray(seg_img)
J=dct2(img);
imshow(log(abs(J)),[]);
%colormap parula
%colorbar
J(abs(J)<10) =0;
K= idct2(J);
K=rescale(K);
%montage({I,K})
%title('Orginal Grayscale Image(left) and processed Image(Right)');
% Create the Gray Level Cooccurance Matrices (GLCMs)
glcms = graycomatrix(K);
% Derive Statistics from GLCM
stats = graycoprops(glcms,'Contrast Correlation Energy Homogeneity');
Contrast = stats.Contrast
Correlation = stats.Correlation
Energy = stats.Energy
Homogeneity = stats.Homogeneity
Mean = mean2(seg_img)
Standard_Deviation = std2(seg_img)
Entropy = entropy(seg_img)
%RMS = mean2(rms(seg_img));
%Skewness = skewness(img)
Variance = mean2(var(double(seg_img)))
a = sum(double(seg_img(:)));
Smoothness = 1-(1/(1+a))
Kurtosis = kurtosis(double(seg_img(:)))
Skewness = skewness(double(seg_img(:)))
% Inverse Difference Movement
m = size(seg_img,1);
n = size(seg_img,2);
in_diff = 0;
for i = 1:m
for j = 1:n
temp = seg_img(i,j)./(1+(i-j).^2);
in_diff = in_diff+temp;
end
end
IDM = double(in_diff)
feat_disease = [Contrast,Correlation,Energy,Homogeneity, Mean, Standard_Deviation, Entropy, Variance, Smoothness, Kurtosis, Skewness, IDM];
%%end
using BLood smear images:
