In this paper we propose a method to classify masses in digital breast tomosynthesis (DBT) datasets. First,
markers of potential lesions are extracted and matched over the different projections. Then two level-set models
are applied on each finding corresponding to spiculated and circumscribed mass assumptions respectively. The
formulation of the active contours within this framework leads to several candidate contours for each finding. In
addition, a membership value to the class <i>contour</i> is derived from the energy of the segmentation model, and
allows associating several fuzzy contours from different projections to each set of markers corresponding to a
lesion. Fuzzy attributes are computed for each fuzzy contour. Then the attributes corresponding to fuzzy contours
associated to each set of markers are aggregated. Finally, these cumulated fuzzy attributes are processed by two
distinct fuzzy decision trees in order to validate/invalidate the spiculated or circumscribed mass assumptions.
The classification has been validated on a database of 23 real lesions using the leave-one-out method. An error
classification rate of 9% was obtained with these data, which confirms the interest of the proposed approach.
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 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.
A novel technique for the detection and enhancement of microcalcifications in digital tomosynthesis mammography (DTM) is presented. In this method, the DTM projection images are used directly, instead of using a 3D reconstruction. Calcification residual images are computed for each of the projection images. Calcification detection is then performed over 3D space, based on the values of the calcification residual images at projection points for each 3D point under test. The quantum, electronic, and tissue noise variance at each pixel in each of the calcification residuals is incorporated into the detection algorithm. The 3D calcification detection algorithm finds a minimum variance estimate of calcification attenuation present in 3D space based on the signal and variance of the calcification residual images at the corresponding points in the projection images. The method effectively detects calcifications in 3D in a way that both ameliorates the difficulties of joint tissue/microcalcification tomosynthetic reconstruction (streak artifacts, etc.) and exploits the well understood image properties of microcalcifications as they appear in 2D mammograms. In this method, 3D reconstruction and calcification detection and enhancement are effectively combined to create a calcification detection specific reconstruction. Motivation and details of the technique and statistical results for DTM data are provided.
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.