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write a matlab code to compare K Means , Mean shift and Fuzzy C clustering algorithms using images.
SOLUTION ::
Step 1: Read Image
he = imread('hestain.png');
imshow(he), title('H&E image');
text(size(he,2),size(he,1)+15,...
'Image courtesy of Alan Partin, Johns Hopkins University', ...
'FontSize',7,'HorizontalAlignment','right');
Step 2: Convert Image from RGB Color Space to L*a*b* Color Space
lab_he = rgb2lab(he);
Step 3: Classify the Colors in 'a*b*' Space Using K-Means Clustering
ab = lab_he(:,:,2:3); ab = im2single(ab); nColors = 3; % repeat the clustering 3 times to avoid local minima pixel_labels = imsegkmeans(ab,nColors,'NumAttempts',3);
imshow(pixel_labels,[]) title('Image Labeled by Cluster Index');
Step 4: Create Images that Segment the H&E Image by Color
mask1 = pixel_labels==1; cluster1 = he .* uint8(mask1); imshow(cluster1) title('Objects in Cluster 1');
mask2 = pixel_labels==2; cluster2 = he .* uint8(mask2); imshow(cluster2) title('Objects in Cluster 2');
mask3 = pixel_labels==3; cluster3 = he .* uint8(mask3); imshow(cluster3) title('Objects in Cluster 3')
Step 5: Segment the Nuclei
L = lab_he(:,:,1); L_blue = L .* double(mask3); L_blue = rescale(L_blue); idx_light_blue = imbinarize(nonzeros(L_blue));
blue_idx = find(mask3); mask_dark_blue = mask3; mask_dark_blue(blue_idx(idx_light_blue)) = 0; blue_nuclei = he .* uint8(mask_dark_blue); imshow(blue_nuclei) title('Blue Nuclei');
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