In this paper, we present a fast method for microcalcification detection in Digital Breast Tomosynthesis. Instead of
applying the straight-forward reconstruction/filtering/thresholding approach, the filtering is performed on projections
before simple back-projection reconstruction. This leads to a reduced computation time since the number of projections
is generally much smaller than the number of slices. For an average breast thickness and a typical number of
projections, the number of operations is reduced by a factor in the range of 2 to 4. At the same time, the approach yields
a negligible decrease of the contrast to noise ratio in the reconstructed slices. Image segmentation results are presented
and compared to the previous method as visual performance assessment.
KEYWORDS: Image segmentation, Digital breast tomosynthesis, Breast, Data modeling, Mammography, Image filtering, Wavelets, Solid modeling, 3D modeling, Visualization
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.
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