Classification of breast masses and normal tissues in digital tomosynthesis mammography

Digital tomosynthesis mammography (DTM) can provide quasi-3D structural information of the breast by reconstructing the breast volume from projection views (PV) acquired in a limited angular range. Our purpose is to design an effective classifier to distinguish breast masses from normal tissues in DTMs. A data set of 100 DTM cases collected with a GE first generation prototype DTM system at the Massachusetts General Hospital was used. We reconstructed the DTMs using a simultaneous algebraic reconstruction technique (SART). Mass candidates were identified by 3D gradient field analysis. Three approaches to distinguish breast masses from normal tissues were evaluated. In the 3D approach, we extracted morphological and run-length statistics texture features from DTM slices as input to a linear discriminant analysis (LDA) classifier. In the 2D approach, the raw input PVs were first preprocessed with a Laplacian pyramid multi-resolution enhancement scheme. A mass candidate was then forward-projected to the preprocessed PVs in order to determine the corresponding regions of interest (ROIs). Spatial gray-level dependence (SGLD) texture features were extracted from each ROI and averaged over 11 PVs. An LDA classifier was designed to distinguish the masses from normal tissues. In the combined approach, the LDA scores from the 3D and 2D approaches were averaged to generate a mass likelihood score for each candidate. The Az values were 0.87±0.02, 0.86±0.02, and 0.91±0.02 for the 3D, 2D, and combined approaches, respectively. The difference between the Az values of the 3D and 2D approaches did not achieve statistical significance. The performance of the combined approach was significantly (p<0.05) better than either the 3D or 2D approach alone. The combined classifier will be useful for false-positive reduction in computerized mass detection in DTM.