Abstract
A new approach to mass detection in mammography is presented. The main obstacle of building a mass detection system is the similar appearance between masses and density tissues in breast. Hence, the various features of the extracted regions of interest (ROIs) are analyzed by synthesis. Then the support vector machine (SVM), which is designed later to distinguish masses from normal areas, is employed to classify these ROIs exactly. To further improve the performance of SVM, the relevance feedback (RF) is introduced to filter out the false positives. The experimental results illustrate that SVM classifier can effectively detect the mass areas, and the RF-SVM scheme can be efficiently incorporated into this learning framework to further improve detection performance.
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