A Novel Clustering Method based on K-MEANS with Region Growing for Micro-calcifications in Mammographic Images

摘要：

Breast cancer is one of the most dangerous malignant tumors of women in the world. A particularly important clue of such disease is the presence of clusters of micro-calcifications. However, it is difficult for radiologists to provide both accurate and uniform evaluation for benign or malignant pathologic modifications of microcalcifications. The radiologists are usually obtained by using human expertise in recognizing the presence of given patterns and types of micro-calcifications. In order to automatically detect such clusters and improve the accuracy, in this paper, K-MEANS-based region growing clustering algorithm is proposed to automatically finding clusters of microcalcifications in the phase of clustering in mammography. The approach has been successfully tested on a standard database of 30 mammographic images, publicly available.

关键词： mammogram cluster automatically K-MEANS

作者: Huanping Zhao Lihua Li Weidong Xu Juan Zhang

作者单位: Institute for Biomedical Engineering and Instrumentation, Hangzhou Dianzi University HangZhou, China Department of Radiology Zhejiang Cancer Hospital HangZhou, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Information Application(2010年计算机与信息应用国际会议 ICCIA 2010)

会议地点: 天津

会议语种:英文

页码: 1-4

在线出版日期: 2010-12-03（万方平台首次上网日期，不代表论文的发表时间）