Mass Detection in Mammogram Based on Marker-pulse Coupled Neural Networks
Han Zhen-zhong① Chen Hou-jin① Li Ju-peng① Yao Chang① Cheng Lin②
①(School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China) ②(Center of Breast Disease, Peking University People’s Hospital, Beijing 100044, China)
Abstract：Mass detection in mammogram plays an important role in early breast cancer diagnosis. A novel method of mass detection in mammogram is proposed. Combined with Pulse Coupled Neural Network (PCNN) model and marker-controlled watershed method, an image slicing method based on Marker-PCNN is presented. Then the suspicious regions are extracted though the Multiple Concentric Layers (MCL) analysis. Finally, the morphological features of mass are employed to eliminate the false positive areas. The experimentation results show that the detected method is excellent and the False Positive (FP) is low. The detection correction rate reached 92.08%. Compared with the original MCL method and Morphological Component Analysis (MCA) method, the proposed method has evident advantage, especially in diagnoses of dense breast cancer.
韩振中, 陈后金, 李居朋, 姚畅, 程琳. 基于标记脉冲耦合神经网络的乳腺肿块分层检测方法[J]. 电子与信息学报, 2013, 35(7): 1664-1670.
Han Zhen-Zhong, Chen Hou-Jin, Li Ju-Peng, Yao Chang, Cheng Lin. Mass Detection in Mammogram Based on Marker-pulse Coupled Neural Networks. , 2013, 35(7): 1664-1670.
2013, Vol. 35 
