Automated Detection of Individual Micro-calcifications from Mammograms using a Multi-stage Cascade Approach
This improves computer-aided detection for breast cancer screening by significantly reducing false positives compared to prior methods, though it is incremental as it builds on existing multi-step frameworks.
The paper tackles the problem of detecting individual micro-calcifications in mammograms by proposing a multi-stage cascade approach that detects and classifies candidates before clustering, achieving a true positive rate of 40% at one false positive per image and 80% at 10 false positives per image on the INbreast dataset.
In mammography, the efficacy of computer-aided detection methods depends, in part, on the robust localisation of micro-calcifications ($μ$C). Currently, the most effective methods are based on three steps: 1) detection of individual $μ$C candidates, 2) clustering of individual $μ$C candidates, and 3) classification of $μ$C 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 $μ$C detections within a certain area. In this paper, we propose a novel approach to $μ$C detection, consisting of the detection \emph{and} classification of individual $μ$C candidates, using shape and appearance features, using a cascade of boosting classifiers. The final step in our approach then clusters the remaining individual $μ$C candidates. The main advantage of this approach lies in its ability to reject a significant number of false positive $μ$C candidates compared to previously proposed methods. Specifically, on the INbreast dataset, we show that our approach has a true positive rate (TPR) for individual $μ$Cs 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 $μ$Cs.