Semiautomatic segmentation for the computer aided diagnosis of clustered microcalcifications

Screening mammography is recognized as the most effective tool for early breast cancer detection. However, its application in clinical practice shows some of its weaknesses. While clustered microcalcifications are often an early sign of breast cancer, the discrimination of benign from malignant clusters based on their appearance in mammograms is a very difficult task. Hence, it is not surprising that typically only 15% to 30% of breast biopsies performed on calcifications will be positive for malignancy. As this low positive predictive value of mammography regarding the diagnosis of calcification clusters results in many unnecessary biopsies performed on benign calcifications, we propose a novel computer aided diagnosis (CADx) approach with the goal to improve the reliability of microcalcification classification. As effective automatic classification of microcalcification clusters relies on good segmentations of the individual calcification particles, many approaches to the automatic segmentation of individual particles have been proposed in the past. Because none of the fully automatic approaches seem to result in optimal segmentations, we propose a novel semiautomatic approach that has automatic components but also allows some interaction of the radiologist. Based on the resulting segmentations we extract a broad range of features that characterize the morphology and distribution of calcification particles. Using regions of interest containing either benign or malignant clusters extracted from the digital database for screening mammography we evaluate the performance of our approach using a support vector machine and ROC analysis. The resulting ROC performance is very promising and we show that the performance of our semiautomatic segmentation is significantly higher than that of a comparable fully automatic approach.