The purpose was to develop a characterization method for breast lesions visible only as microcalcifications on digital mammography. The method involved 4 steps: 1) image preprocessing through morphological filtering, 2) un-supervised identification of microcalcifications in the region surrounding the radiologist-indicated location through k-means clustering, 3) segmentation of the identified microcalcifications using an active contour model, and 4) characterization by computer-extracted image-based phenotypes describing properties of individual microcalcifications, cluster, and surrounding parenchyma. The image-based phenotypes were investigated for their ability to distinguish – individually, i.e., without merging with other phenotypes with a classifier – between invasive breast cancers, in-situ (non-invasive) breast cancers, fibroadenomas, and other benign-type lesions. The data set contained diagnostic mammograms of 82 patients with 2 views per patient – cranio-caudal (CC) and medio-lateral (ML) views of the affected breast with a single biopsy-proven finding indicated per view – with 7 invasive cancers, 14 in situ cancers, 13 fibroadenomas, and 48 other benign-type lesions. Analysis was performed per lesion and calculated phenotypes were averaged over views. Performance was assessed using ROC analysis with individual phenotypes as decision variables in the tasks of a) pairwise distinction amongst the 4 finding types, b) distinction between each finding type and all others, and c) distinction between cancer and non-cancer. Different phenotypes emerged as the best performers with areas under the ROC curve ranging from 0.69 (0.05) to 0.92 (0.09) depending on the task. We obtained encouraging preliminary results beyond the classification of cancer versus non-cancer in the distinction between different types of breast lesions visible as mammographic calcifications.