A novel method of mass segmentation in mammogram
Zhenzhong Han, Houjin Chen, Jupeng Li, Chang Yao · 2012
Mass detection in mammogram is one of effective technology for breast cancer diagnosis. A novel method of mass segmentation in mammogram is proposed in this paper. First, a mathematical model (MM) of the mass is presented to detect the location of mass. Second, based on the time series features generated by Pulse Coupled Neural Network (PCNN), the pixels are classified by Fuzzy C-Means clustering (FCM) algorithm. Last, combining the location and the cluster results, segmentation of masses can be achieved effectively. The experimental results show that mass detected by this method is accurate and the false positive (FP) rate is very low. The detection rate of masses reached 98.82%.