Simplified false-positive reduction in computer-aided detection scheme of clustered microcalcifications in digital breast tomosynthesis

A computer-aided detection (CADe) system for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) volumes was suggested. The system consisted of prescreening, MC detecting, clustering, and false-positive reduction steps. In the prescreening stage, the MC-like objects were enhanced by a multiscale-based 3D calcification response function. A connected component segmentation method was used to detect cluster seed objects, which were considered as potential clustering centers of MCs. Starting with each cluster seed object as the initial cluster center, a cluster candidate was formed by including nearby MC candidates within a 3D neighborhood of the cluster seed object satisfying the clustering criteria during the clustering step. The size and number of the clustered MCs in a cluster seed candidate were used to reduce the number of FPs. A bounding cube for each MCC was generated for each accepted seed candidates. Then, the overlapping cubes were combined and examined according to the FP reduction criteria. After FP reduction step, we obtained the average number of FPs of 2.47 per DBT volume with sensitivity of 83.3%. Our study indicates the simplified false-positive reduction approach applied to the detection of clustered MCs in DBT is promising as an efficient CADe system.