False-positive reduction using RANSAC in mammography microcalcification detection

This paper proposes a method for false-positive reduction in mammography computer aided detection (CAD) systems by detecting a linear structure (LS) in individual microcalcification (MCC) cluster candidates, which primarily involves three steps. First, it applies a modified RANSAC algorithm to a region of interest (ROI) that encloses an MCC cluster candidate to find LS. Second, a peak-to-peak ratio of two orthogonal integral-curves (named the RANSAC feature) is computed based on the results from the first step. Last, the computed RANSAC feature is, together with other MCC cancer features, used in a neural network for MCC classification, results of which are compared with the classification without the RANSAC feature. One thousand (1000) cases were used in training the classifiers, 671 cases were used in testing. The comparison shows that there is a significant improvement in terms of the reduction of linear structure associated false-positives readings (up to about 40% FP reduction).

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