Computer aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures

The purpose of this project is to study two Computer Aided Detection (CADe) systems for breast masses for digital tomosynthesis using reconstructed slices. This study used eighty human subject cases collected as part of on-going clinical trials at Duke University. Raw projections images were used to identify suspicious regions in the algorithm's high sensitivity, low specificity stage using a Difference of Gaussian filter. The filtered images were thresholded to yield initial CADe hits that were then shifted and added to yield a 3D distribution of suspicious regions. The initial system performance was 95% sensitivity at 10 false positives per breast volume. Two CADe systems were developed. In system A, the central slice located at the centroid depth was used to extract a 256× 256 Regions of Interest (ROI) database centered at the lesion coordinates. For system B, 5 slices centered at the lesion coordinates were summed before the extraction of 256× 256 ROIs. To avoid issues associated with feature extraction, selection, and merging, information theory principles were used to reduce false positives for both the systems resulting in a classifier performance of 0.81 and 0.865 Area Under Curve (AUC) with leave-one-case-out sampling. This resulted in an overall system performance of 87% sensitivity with 6.1 FPs/volume and 85% sensitivity with 3.8 FPs/ volume for systems A and B respectively. This system therefore has the potential to detect breast masses in tomosynthesis data sets.