Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach

In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of microcalcifications (μC). Currently, the most effective methods are based on three steps: 1) detection of individual μC candidates, 2) clustering of individual μC candidates, and 3) classification of μC clusters. Where the second step is motivated both to reduce the number of false positive detections from the first step and on the evidence that malignancy depends on a relatively large number of μC detections within a certain area. In this paper, we propose a novel approach to μC detection, consisting of the detection and classification of individual μC candidates, using shape and appearance features, using a cascade of boosting classifiers. The final step in our approach then clusters the remaining individual μC candidates. The main advantage of this approach lies in its ability to reject a significant number of false positive μC candidates compared to previously proposed methods. Specifically, on the INbreast dataset, we show that our approach has a true positive rate (TPR) for individual μCs of 40% at one false positive per image (FPI) and a TPR of 80% at 10 FPI. These results are significantly more accurate than the current state of the art, which has a TPR of less than 1% at one FPI and a TPR of 10% at 10 FPI. Our results are competitive with the state of the art at the subsequent stage of detecting clusters of μCs.