Abstract The paper describes three different techniques for the segmentation of biomedical images based on Entropy (ISE method), Fuzzy Entropy (FISE method) and the Least Square method (LHIS method). The relation between the entropy of an image and the entropy of its subdomains is explored as a uniformity predicate. Such entropy is obtained from an analysis of the image histogram associating a Gaussian distribution to the maximum frequency of grey levels. The experimental results show the comparison between the three proposed segmentation schemes, putting in evidence favourable time requirements and the subjective quality of the segmented images.
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