Fuzzy Entropy Based Detection of Suspicious Masses in Digital Mammogram Images

Abstract

Mammography is the standard method for screening and detecting breast abnormalities. In this paper, we propose a novel scheme for suspicious lesion detection in digital mammograms. The proposed scheme is based on image thresholding. The optimal threshold is determined by minimizing the fuzzy entropy of the image. Moreover, the paper introduces a new block-based performance criterion to compare between the computer generated and the radiologist segmented images. Experimental results over a set of sample images showed that the proposed scheme produces accurate segmentation results when compared with the manual results produced by radiologists. Hence the proposed scheme can be used as an effective tool in monitoring and detecting suspicious lesions on digital mammogram images.