KEYWORDS: 3D modeling, Tumor growth modeling, Clouds, Binary data, Computer aided diagnosis and therapy, 3D image processing, Breast, Digital breast tomosynthesis, Feature extraction, Solid modeling
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 microcalcications (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 microcalcication (MC) clusters for classifying benign and malignant cases. Point clouds were initially generated from tomosynthesis slices. Two additional oset 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 laice. Aer modeling individual MCs into a unit cubic laice, 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% classication accuracy.
Microcalcifications (MC) are small deposits of calcium, which are associated with early signs of breast cancer. In this paper, a novel approach is presented to develop a computer-aided diagnosis (CADx) system for automatic differentiation between benign and malignant MC clusters based on their morphology, texture, and the distribution of individual and global features using an ensemble classifier. The images were enhanced, segmented and the feature extraction and selection phase were carried out to generate the feature space which was later fed into an ensemble classifier to classify the MC clusters. The validity of the proposed method was investigated by using two well-known digitized datasets that contain biopsy proven results for MC clusters: MIAS (24 images: 12 benign, 12 malignant) and DDSM (280 images: 148 benign and 132 malignant). A high classification accuracies (100% for MIAS and 91.39% for DDSM) and good ROC results (area under the ROC curve equal to 1 for MIAS and 0.91 for DDSM) were achieved. A full comparison with related publications is provided. The results indicate that the proposed approach is outperforming the current state-of-the-art methods.
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