Three-dimensional modeling of microcalcification clusters using breast tomosynthesis: a preliminary study

Computer aided diagnosis (CADx) systems for digital mammography mostly rely on 2D techniques. With the emergence of three-dimensional (3D) breast imaging modalities, such as digital breast tomosynthesis (DBT), there is an opportunity to create 3D models and analyze 3D features to classify microcalci€cations (MC) clusters to help the early detection of breast cancer. We adopted the 3L algorithm for implicit B-spline (IBS) €‹ing to investigate the robustness of 3D models of microcalci€cation (MC) clusters for classifying benign and malignant cases. Point clouds were initially generated from tomosynthesis slices. Two additional o‚set points were generated to support the original point clouds for detailed 3D modeling. Before €‹ing the splines, the point clouds were normalized into a unit cube la‹ice. A‰er modeling individual MCs into a unit cubic la‹ice, they are all located in a 3D space according to their spatial location in the tomosynthesis images to form a cluster. Features were extracted from the 3D model of MC clusters. With selected features we obtained 80% classi€cation accuracy.

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