In this study we propose a Computer Aided Detection and Diagnosis System to detect breast cancer based on characteristics of individual microcalcifications (main indicators of an early breast cancer) by scanning breast tissue with micro-CT, a high resolution 3D imaging modality. By integrating supervised machine learning techniques with feature extraction and feature selection methods, we are able to classify MCs as benign or malignant with 75.88% accuracy, 62.13% sensitivity and 86.39% specificity, outperforming the state of the art.