Combination of block difference inverse probability features and support vector machine to reduce false positives in computer-aided detection for massive lesions in mammographic images

A new false positive reduction approach in computer-aided mammographic mass detection has been proposed in this paper. The goal is to discriminate true recognized masses from the normal parenchyma ones. To describe masses, Block Difference Inverse Probability (BDIP) features are utilized. Once the descriptors are extracted, we use Support Vector Machine (SVM) to classify the detected masses. Evaluation on about 2700 suspicious regions detected from Mini-MIAS database gives the discrimination result of 0.91. It indicates that using BDIP features is effective and efficient for reducing false positives.