Fuzzy logic and scale space approach to microcalcification detection

Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of mammograms generated in widespread screening. Microcalcifications and masses are the earliest signs of breast carcinomas and their detection is one of the key issues for breast cancer control. Computer-aided detection of microcalcifications and masses is an important and challenging task in breast cancer control. This paper presents a novel approach for detecting microcalcification clusters (MCCs). First, mammograms are normalized. Then, fuzzy set theory and fuzzy entropy principle are employed to fuzzify the mammograms. Then, the fuzzified images are enhanced. Finally, scalespace and Laplacian-of-a-Gaussian filter techniques are used to determine the sizes and locations of microcalicifications. A free-response operating characteristic (FROC) curve is used to evaluate the performance. The major advantage of the proposed system is its ability to detect microcalcifications even in very dense breast mammograms. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications is studied. Experimental result show that the microcalcifications can be accurately and efficiently detected using the proposed approach.