Abstract
Segmentation is a foremost part of computer-aided diagnosis (CAD) and medical image analysis. This paper provides an automatic threshold selection approach for the segmentation of medical images based on fuzzy 2-partition using Kapur entropy. The fuzzy 2-partition Kapur entropy approach converts the image into fuzzy 2-partition by applying two parameterised fuzzy membership functions. The optimal threshold is attained by searching for an ideal combination of the parameter for the fuzzy membership functions so that the fuzzy 2-partition Kapur entropy is maximised. The complexity of searching the optimal combination of parameters is reduced by integrating the concept of fast recursive algorithm and Kapur entropy. The proposed approach performance is measured using several medical images and it is found that results are considerable encouraging. The proposed approach could be put up as a component of a CAD system for early detection of diseases like cancer.
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