Mammogram Density Estimation using Sub-Region Classification

摘要：

Breast density is a widely adopted measure for early breast cancer diagnose. In this paper, an automated breast density estimation method was proposed. Mammograms were analyzed using wavelet transform to extract tissue-like contents. A tissue image was then divided into fixed size sub-regions. The sub-regions were classified as high and low density categories using their distribution features. In this paper, groups of histogram moments were extracted as features of sub-regions, and served as inputs of the support vector machine (SVM) for classification. The breast density of the whole mammogram was then evaluated by calculating the ratio of number of high density sub-regions to that of the whole set. Experimental results show the excellent performance of the proposed method.

关键词： breast density multiscale analysis histogram moment sub-region classification support vector machine

作者: Qingqing Liu Li Liu Yanli Tan Jian Wang Xueyun Ma Hairi Ni

作者单位: School of Elec.& Info. Eng.Tianjin UniversityTianjin, China

会议类型: 国际会议

会议名称: 2011 4th International Conference on Biomedical Engineering and Informatics(第四届生物医学工程与信息学国际会议 BMEI 2011)

会议地点: 上海

会议语种:英文

页码: 356-359

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）