物理学报Issue(7)：408-418,11.DOI:10.7498/aps.63.078703
基于SPCNN与改进型矢量CV模型的乳腺X射线肿块分割方法
Mass segmentation in mammogram based on SPCNN and improved vector-CV
摘要
Abstract
Mass segmentation plays an important role in computer-aided diagnosis (CAD) system. The segmentation result seriously affects classifying mass as benign and malignant. By combining the simplified pulse coupled neural network (SPCNN) and the improved vector active contour without edge (vector-CV), a novel method of mass segmentation in mammogram is proposed in this paper. First, the parameters and termination conditions of SPCNN are obtained through mathematical analysis and the initial contour is segmented by SPCNN. Then, the vector CV model is accordingly modified to overcome the shortcomings of traditional CV model. Finally, combined with the initial contour, the improved vector-CV is used to segment the mass contour. The experiments implemented on the public digital database for screening mammography (DDSM) and the clinical images which are provided by the Center of Breast Disease of Peking University People’s Hospital indicate that the proposed method is better than the existing methods, especially when dealing with the dense breasts of Oriental female.关键词
计算机辅助诊断/肿块分割/简化型脉冲耦合神经网络/矢量无边缘活动轮廓模型Key words
CAD/mass segmentation/SPCNN/vector-CV引用本文复制引用
韩振中,陈后金,李艳凤,李居朋,姚畅,程琳..基于SPCNN与改进型矢量CV模型的乳腺X射线肿块分割方法[J].物理学报,2014,(7):408-418,11.基金项目
国家自然科学基金(批准号:61271305,61201363,60972093)、国家高等学校博士学科点专项科研基金(批准号:20110009110001)和中央高校基本科研业务费专项资金(批准号:2014JBM020)资助的课题.* Project supported by the National Natural Science Foundation of China (Grant Nos.661271305,61201363,60972093), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No.20110009110001), and the Fundamental Research Funds for the Central Universities (Grant No.2014JBM020) （批准号:61271305,61201363,60972093）
