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In this paper we present a novel approach for mass contour detection for 3D computer-aided detection (CAD) in digital breast tomosynthesis (DBT) data-sets. A hybrid active contour model, working directly on the projected views, is proposed. The responses of a wavelet filter applied on the projections are thresholded and combined to obtain markers for mass candidates. The contours of markers are extracted and serve as initialization for the active contour model, which is then used to extract mass contours in DBT projection images. A hybrid model is presented, taking into account several image-based external forces and implemented using a level-set formulation. A feature vector is computed from the detected contour, which may serve as input to a dedicated classifier. The segmentation method is applied to simulated images and to clinical cases. Image segmentation results are presented and compared to two standard active contour models. Evaluation of the performance on clinical data is obtained by comparison to manual segmentation by an expert. Performance on simulated images and visual performance assessment provide further illustration of the performance of the presented approach.

In this paper we present a novel approach for mass detection in Digital Breast Tomosynthesis (DBT) datasets. A reconstruction-independent approach, working directly on the projected views, is proposed. Wavelet filter responses on the projections are thresholded and combined to obtain candidate masses. For each candidate, we create a fuzzy contour through a multi-level thresholding process. We introduce a fuzzy set definition for the class mass contour that allows the computation of fuzzy membership values for each candidate contour. Then, an aggregation operator is presented that combines information over the complete set of projected views, resulting in 3D fuzzy particles. A final decision is made taking into account all available information. The performance of the presented algorithm was evaluated on a database of 11 one-breast-cases resulting in a sensitivity (Se) of 0.86 and a false positive rate (FPR) of 3.5 per case.

In this paper we present a novel approach for microcalcification detection in Digital Breast Tomosynthesis (DBT) datasets. A reconstructionindependent approach, working directly on the projected views, is proposed. Wavelet filter responses on the projections are thresholded and combined to obtain candidate microcalcifications. For each candidate, we create a fuzzy contour through a multi-level thresholding process. We introduce a fuzzy set definition for the class microcalcification contour that allows the computation of fuzzy membership values for each candidate contour. Then, an aggregation operator is presented that combines information over the complete set of projected views, resulting in 3D fuzzy particles. A final decision is made taking into account information acquired over a range of successive processing steps. A clinical example is provided that illustrates our approach. DBT still being a new modality, a similar published approach is not available for comparison and limited clinical data currently prevents a clinical evaluation of the algorithm. .

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