Discriminant Subspace Learning for Microcalcification Clusters Detection

摘要：

This paper presents a novel approach to microcalcification clusters (MCs) detection in mammograms based on the discriminant subspace learning. The ground truth of MCs in mammograms is assumed to be known as a priori. Several typical subspace learning algorithms, such as principal component analysis (PCX), linear discriminant analysis (LDA), tensor subspace analysis (TSA) and general tensor discriminant Analysis (GTDA), are employed to extract subspace features. In subspace feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and SVM is used as a classifier to make decision for the presence of MCs or not A large number of experiments are carried out to evaluate and compare the performance of the proposed MCs detection algorithms. The experiment result suggests that correlation filters is a promising technique for MCs detection.

关键词： microcalcification twin support vector machines subspace learning principal component analysis general tensor discriminant analysis

作者: X.-S.Zhang Hua Xie

作者单位: School of Management Xian University of Architecture and Technology Xian, Shaan Xi Province, China College of Foreign Languages Xi an International University Xian, Shaan Xi Province, China

会议类型: 国际会议

会议名称: 2010 International Conference on Circuit and Signal Processing(2010年电路与信号处理国际会议 ICCSP 2010)

会议地点: 上海

会议语种:英文

页码: 328-332

在线出版日期: 2010-12-25（万方平台首次上网日期，不代表论文的发表时间）