In this paper, a novel microcalcification detection in Digital Breast Tomosynthesis (DBT) is proposed, which allows detecting subtle microcalcifications that have relatively low contrast in DBT slices. 3D Foveal segmentation is employed for volume of interest (VOI) detection using adaptive thresholding scheme by local volume contrast. Furthermore, in order to conserve structural characteristics of clustered microcalcifications, features from VOI maximum ray-tracing images with three orthogonal directions are extracted. Extensive and comparative experiments were carried out to demonstrate the effectiveness of the proposed 3D Foveal segmentation and the feature extraction method on clinical DBT cases. The results showed that the proposed microcalcification detection achieves a sensitivity of 98% at 0.5 FP per volume.