Question

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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 ::

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;
imshow(cluster1)
title('Objects in Cluster 1');```
```mask2 = pixel_labels==2;
imshow(cluster2)
title('Objects in Cluster 2');```
```mask3 = pixel_labels==3;
imshow(cluster3)
title('Objects in Cluster 3')```

Step 5: Segment the Nuclei

```L = lab_he(:,:,1);
L_blue = rescale(L_blue);
idx_light_blue = imbinarize(nonzeros(L_blue));```
```blue_idx = find(mask3);

imshow(blue_nuclei)
title('Blue Nuclei');```

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