A computer-aided detection (CADe) algorithm for clustered microcalcifications (MCs) in reconstructed digital breast tomosynthesis (DBT) images is suggested. The MC-like objects were enhanced by a Hessian-based 3D calcification response function, and a signal-to-noise ratio (SNR) enhanced image was also generated to screen the MC clustering seed objects. A connected component segmentation method was used to detect the cluster seed objects, which were considered as potential clustering centers of MCs. Bounding cubes for the accepted clustering seed candidate were generated and the overlapping cubes were combined and examined. After the MC clustering and false-positive (FP) reduction step, the average number of FPs was estimated to be 0.87 per DBT volume with a sensitivity of 90.5%.