A Support Vector Machine Approach for Detection of Microcalcifications

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the S...

• Type: Journal
• Year: 2002
• Where: TMI
• Authors: Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Nikolas P. Galatsanos, Robert M. Nishikawa
• Keywords: Detection, MC Detection, Support Vector Machines, TMI 2002