Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with their gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value<0.001 returned by jackknife FROC analysis performed on the two CAD systems