can i use k-means algorithm for segmenting the cell nucleus and cytoplasm?

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i have used k-means clustering algorithm for segmenting cells but it doesn't. i didn't know about k-means algorithm in detail. i don't know whether this algorithm suits or not. suggest me a way. i have attached my image that i have been working. the inner dark region is nucleus and outer region is cytoplasm.

Accepted Answer

Image Analyst
Image Analyst on 7 Mar 2015
Image Analyst
Image Analyst on 21 Apr 2015
sou, then you need to improve the color segmentation algorithm.
Braiki, I'm not sure what you said. Something about errors, but you didn't provide the errors or the code.

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More Answers (1)

Alex Taylor
Alex Taylor on 23 Apr 2015
Edited: Alex Taylor on 23 Apr 2015
In this case, I found that I was able to get a reasonably good segmentation of the nucleus by working directly in the RGB colorspace instead of LAB as is done in the example:
%%Read and display input image
A = imread('');
A = im2double(A);
numRows = size(A,1);
numCols = size(A,2);
numPoints = numRows*numCols;
X = reshape(A,numRows*numCols,[]);
Normalize features to be zero mean, unit variance
X = bsxfun(@minus, X, mean(X));
X = bsxfun(@rdivide,X,std(X));
%%Classify color features using kmeans
% Repeat k-means clustering five times to avoid local minima when searching
% for means that minimize objective function. The only prior information
% assumed in this example is how many distinct regions of texture are
% present in the image being segmented. There are two distinct regions in
% this case.
L = kmeans(X,3,'Replicates',5);
%%Visualize segmentation using |label2rgb|
L = reshape(L,[numRows numCols]);
%%Visualize segmented image using |imshowpair|
% Use imshowpair to examine the foreground and background images that
% result from the mask BW that is associated with the label matrix L.
Aseg1 = zeros(size(A),'like',A);
Aseg2 = zeros(size(A),'like',A);
BW = L == 2;
BW = repmat(BW,[1 1 3]);
Aseg1(BW) = A(BW);
Aseg2(~BW) = A(~BW);

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