Automatic Dual-View Mass Detection in Full-Field Digital Mammograms

Mammography is the first-line modality for screening and diagnosis of breast cancer. Following the common practice of radiologists to examine two mammography views, we propose a fully automated dual-view analysis framework for breast mass detection in mammograms. The framework combines unsupervised segmentation and random-forest classification to detect and rank candidate masses in cranial-caudal (CC) and mediolateral-oblique (MLO) views. Subsequently, it estimates correspondences between pairs of candidates in the two views. The performance of the method was evaluated using a publicly available full-field digital mammography database (INbreast). Dual-view analysis provided area under the ROC curve of 0.94, with detection sensitivity of 87% at specificity of 90%, which significantly improved single-view performance (72% sensitivity at 90% specificity, 78% specificity at 87% sensitivity, P<0.05). One-to-one mapping of candidate masses from two views facilitated correct estimation of the breast quadrant in 77% of the cases. The proposed method may assist radiologists to efficiently identify and classify breast masses.