Detection of masses and architectural distortions in digital breast tomosynthesis images using fuzzy and a contrario approaches

Digital breast tomosynthesis (DBT) is a new 3D imaging technique, which overcomes some limitations of traditional digital mammography. Its development induces an increased amount of data to be processed, thus calling for a computer aided detection system to help the radiologist. Towards this aim, this paper focuses on the detection of masses and architectural distortions in DBT images. A complete detection scheme is proposed, consisting of two parts, called channels, each dedicated to one type of lesions, which are then merged in a final decision step, thus handling correctly the potential overlap between the two types of lesions. The first detection channel exploits the dense kernel nature of masses and the intrinsic imprecision of their attributes in a fuzzy approach. The second detection channel models the convergence characteristics of architectural distortions in an a contrario approach. The experimental results on 101 breasts, including 53 lesions, demonstrate the usefulness of the proposed approach, which leads to a high sensitivity with a reduced number of false positives, and compares favorably to existing approaches.