Automatic detection of microcalcifications using mathematical morphology and a support vector machine
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: Department of Information Science, Xi'an University of Technology, Xi'an, Shaanxi, 710048, China
Note: [] E-mail: eh-zhang@xaut.edu.cn.
Abstract: In this paper, we propose a novel method for the detection of microcalcifications using mathematical morphology and a support vector machine (SVM). First, the contrast in the original mammogram was improved by gamma correction and two carefully designed structural elements were used to enhance any microcalcifications. Next, the potential regions were extracted using our proposed dual-threshold technique. Finally, a SVM classifier was used to reduce the number of false positives. The performance of the proposed method was evaluated using the MIAS database. The experimental results demonstrated the efficiency and effectiveness of our method.
Keywords: feature extraction, mathematical morphology, microcalcification, support vector machine
