20060602-Cell segmentation

原文：http://blogs.mathworks.com/steve/2006/06/02/cell-segmentation/

Note: you can download the functions
imcreit and
imoverlay from MATLAB Central.

I = imread('http://blogs.mathworks.com/images/steve/60/nuclei.png');
I_cropped = I(400:900, 465:965);
imshow(I_cropped)
imcredit('Image courtesy of Dr. Ramiro Massol')

Strictly speaking, contrast adjustment isn't usually necessary for segmentation, but it can help the algorithm developer see and understand the image data better. This is a fairly low-contrast image, so I thought it might help. You can adjust
the display contrast interactively with
imtool, or you can use an automatic method such as
adapthisteq. adapthisteq implements a technique called
contrast-limited adaptive histogram equalization, or CLAHE. (I always thought "CLAHE" sounded like it must be some Klingon delicacy.)

I_eq = adapthisteq(I_cropped);
imshow(I_eq)

So what happens if we just apply a threshold now?

bw = im2bw(I_eq, graythresh(I_eq));
imshow(bw)

Let's clean that up and then overlay the perimeter on the original image.

bw2 = imfill(bw,'holes');
bw3 = imopen(bw2, ones(5,5));
bw4 = bwareaopen(bw3, 40);
bw4_perim = bwperim(bw4);
overlay1 = imoverlay(I_eq, bw4_perim, [.3 1 .3]);
imshow(overlay1)

Now, I'm not familiar with these cell images, so I don't know exactly what I'm looking at. I assume some of these blobs need more help to be separated properly. One possible approach is called
marker-based watershed segmentation.

Let's try to use the bright objects, which I assume are nuclei. The extended maxima operator can be used to identify groups of pixels that are significantly higher than their immediate surrounding.

mask_em = imextendedmax(I_eq, 30);
imshow(mask_em)

Let's clean that up and then overlay it.

mask_em = imclose(mask_em, ones(5,5));
mask_em = imfill(mask_em, 'holes');
mask_em = bwareaopen(mask_em, 40);
overlay2 = imoverlay(I_eq, bw4_perim | mask_em, [.3 1 .3]);
imshow(overlay2)

Next step: complement the image so that the peaks become valleys. We do this because we are about to apply the watershed transform, which identifies low points, not high points.

I_eq_c = imcomplement(I_eq);

Next: modify the image so that the background pixels and the extended maxima pixels are forced to be the only local minima in the image.

I_mod = imimposemin(I_eq_c, ~bw4 | mask_em);

Now compute the watershed transform.

L = watershed(I_mod);
imshow(label2rgb(L))