Neuro-Fuzzy Approach to Microcalcification Contrast Enhancement in Digitized Mammogram Images

Abstract

Computer aided diagnoses can assists radiologists in detecting microcalcification, crucial evidence in mammogram for the early diagnosis of breast cancer. A novel approach is proposed in this paper for early detection of breast cancer by enhancing microcalcification regions in mammogram images using hybrid neuro-fuzzy technique. As a first stage, the mammogram intensities are fuzzified using three linguistic labels. Then, the inference engine of a classical fuzzy system is replaced by a collection of sixteen parallel neural networks and a cascade neural network in order to reduce the computational time for real-time applications. The parallel cascade neural networks are trained using data sets that randomly selected from the original fuzzy decision matrix. Finally, the value of the local mask centre is enhanced after defuzzification the input sets. This work is extensively evaluated using two different types of resources which are Mammographic Image Analysis Society database (MIAS) and University of South Florida (USF) database. As a result, it found to be sensitive in enhancing the microcalcifications regions in mammogram with very little number false positive regions.