A novel clustering method based on K-MEANS with region growing for micro-calcifications in mammographic images
Zhao Huanping, Lihua Li, Weidong Xu, Juan Zhang · 2010
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 micro-calcifications. 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 micro-calcifications in the phase of clustering in mammography. The approach has been successfully tested on a standard database of 30 mammographic images, publicly available.