Title :
A novel method of mass segmentation in mammogram
Author :
Han, Zhen-zhong ; Chen, Hou-jin ; Li, Ju-peng ; Yao, Chang
Author_Institution :
Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, China
Abstract :
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%.
Keywords :
cancer; fuzzy set theory; image segmentation; mammography; medical image processing; neural nets; pattern clustering; time series; FCM algorithm; PCNN; for breast cancer diagnosis; fuzzy c-means clustering algorithm; mammogram; mass detection; mass segmentation; mathematical model; pulse coupled neural network; time series features; Breast cancer; Classification algorithms; Clustering algorithms; Image segmentation; Mathematical model; Time series analysis; Breast cancer; FCM; Mammogram; Mass segmentation; Mathematical model; PCNN;
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
DOI :
10.1109/ICSAI.2012.6223300
