Question

write a matlab code to compare K Means, Mean shift and Fuzzy C clustering algorithms using images

Answer 1

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AS FOR GIVEN DATA...

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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