Fuzzy entropy threshold approach to breast cancer detection

Abstract

Thresholding plays an important role in image processing. To select a suitable threshold requires some criteria on which to base the selection. A criterion of maximum fuzzy entropy is developed for selecting the threshold. In this algorithm, the degree of ambiguity in an image is measured by the entropy of a fuzzy set. The threshold is selected by maximizing the fuzzy entropy of the image. The effectiveness of the algorithm is demonstrated for different bandwidths of the membership functions using noisy and vague microscopic-slide breast cancer images. The results show that this method is useful for breast cancer detection. Moreover, this method can be applied to a wide range of image processing applications.