First of all,
imdata=reshape(grayscaleImage,[],1);
imdata=double(imdata);
can be done with the (:) operation more simply as this:
imdata=double(grayscaleImage(:));
Next, the way you use kmeans and fcm are essentially the same - you're trying to come up with a threshold to split the image into foreground and background. That's essentially what imbinarize() does. If the image has two clear, well separated modes to the histogram, then all 3 methods may well decide upon the same threshold and give you the same foreground and background classes. If the thresholds are slightly different then they may give you very similar, but not exactly the same, images. So that's why both methods you tried are essentially no different than one another.
Next, there is no guarantee that automatic thresholding will be robust. For example, what if your image is of completely normal skin? You'd expect it to say there is no skin cancer. However your automatic thresholding methods will be forced to divide the image into two classes, and you're assuming one class is cancer. To avoid that you're going to have to say that if the means of the two classes are close, then essentially there is only one class, not two. Then you have to decide if that image is 100% normal or 100% cancer. If you require that your field of view has at least half the pixels be normal, then if there is just one class you can assume that it is normal, not cancerous.
