Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume

In this paper, we propose bilateral features for classifying breast masses by extracting the asymmetric information of both the left and the right breasts in the digital breast tomosynthesis (DBT) reconstructed volume. Clinically, it is known that the left and the right breast of the same patient tend to present a high degree of symmetry of internal structures over broad areas. On the other hand, masses appear as asymmetric densities which show different breast tissue structures between the left and the right breasts. Based on that clinical fact, bilateral features are proposed to measure the dissimilarity of texture or intensity characteristics between volumes-of-interest (VOIs) in a given breast, and the corresponding VOIs in the bilateral breast. Experimental results show that the proposed bilateral features in conjunction with single-view mass features can achieve higher level of classification performance in terms of the area under the receiver operating characteristic (ROC) curve (AUC) compared to the performance of the single-view features only.