Mammography is widely recognized as the most reliable technique for early detection of breast cancers. Automated or semi-automated computerized classification schemes can be very useful in assisting radiologists with a second opinion about the visual diagnosis of breast lesions, thus leading to a reduction in the number of unnecessary biopsies. We present a computer-aided diagnosis (CADi) system for the characterization of massive lesions in mammograms, whose aim is to distinguish malignant from benign masses. The CADi system we realized is based on a three-stage algorithm: a) a segmentation technique extracts the contours of the massive lesion from the image; b) sixteen features based on size and shape of the lesion are computed; c) a neural classifier merges the features into an estimated likelihood of malignancy. A dataset of 226 massive lesions (109 malignant and 117 benign) has been used in this study. The system performances have been evaluated terms of the receiver-operating characteristic (ROC) analysis, obtaining A_z = 0.80+-0.04 as the estimated area under the ROC curve.
@article{arxiv.physics/0701053,
title = {An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms},
author = {A. Retico and P. Delogu and M. E. Fantacci and P. Kasae},
journal= {arXiv preprint arXiv:physics/0701053},
year = {2007}
}
Comments
6 pages, 3 figures; Proceedings of the ITBS 2005, 3rd International Conference on Imaging Technologies in Biomedical Sciences, 25-28 September 2005, Milos Island, Greece