An Alternative Approach to Reduce Massive False Positives in Mammograms Using Block Variance of Local Coefficients Features and Support Vector Machine

Computer Aided Detection (CAD) systems for detecting lesions in mammograms have been investigated because the computer can improve radiologists’ detection accuracy. However, the main problem encountered in the development of CAD systems is a high number of false positives usually arise. It is particularly true in mass detection. Different methods have been proposed so far for this task but the problem has not been fully solved yet. In this paper, we propose an alternative approach to perform false positive reduction in massive lesion detection. Our idea is lying in the use of Block Variation of Local Correlation Coefficients (BVLC) texture features to characterize detected masses. Then, Support Vector Machine (SVM) classifier is used to classify the detected masses. Evaluation on about 2700 RoIs (Regions of Interest) detected from Mini-MIAS database gives an accuracy of Az = 0.93 (area under Receiving Operating Characteristics curve). The results show that BVLC features are effective and efficient descriptors for massive lesions in mammograms.