Texture-Based Feature Extraction for the Microcalcification from Digital Mammogram Images

This paper describes our ongoing efforts to provide efficient and accurate classification of microcalcification clusters in mammogram images. In this paper, a study of the characteristics of true microcalcifications compared to falsely detected microcalcifications is carried out using first and second order statistical texture analysis techniques. These features are generated in order to reduce the false positive (FP) ratio for the mammogram images. The statistical method presented here can successfully reduce the ratio of false positives (FP) by 18% without affecting the ratio of true positives (TP) which is currently at 98%.