Computer aided diagnosis (CADx) systems for digital mammography mostly rely on 2D techniques. With the emergence of three-dimensional (3D) breast imaging modalities, such as digital breast tomosynthesis (DBT), there is an opportunity to create 3D models and analyze 3D features to classify microcalcifications (MC) clusters to help the early detection of breast cancer. We adopted the 3L algorithm for implicit B-spline (IBS) fitting to investigate the robustness of 3D models of microcalcification (MC) clusters for classifying benign and malignant cases. Point clouds were initially generated from tomosynthesis slices. Two additional oÂ?set points were generated to support the original point clouds for detailed 3D modeling. Before fitting the splines, the point clouds were normalized into a unit cube lattice. AÂ‰er modeling individual MCs into a unit cubic lattice, they are all located in a 3D space according to their spatial location in the tomosynthesis images to form a cluster. Features were extracted from the 3D model of MC clusters. With selected features we obtained 80% classification accuracy.