COMPUTERIZED DETECTION OF MASSES IN DIGITIZED MAMMOGRAMS USING SINGLE-IMAGE SEGMENTATION AND A MULTILAYER TOPOGRAPHIC FEATURE ANALYSIS

Rationale and Objectives. We developed and evaluated a computer-aided detection (CAD) scheme for masses in digitized mammograms. Methods. A multistep CAD scheme was developed and tested. The method uses a technique of single-image segmentation with Gaussian bandpass filtering to yield a high sensitivity for mass detection. A rule-based multilayer topographic feature analysis method is then used to classify suspected regions. A set of 260 cases, including 162 verified masses, was divided into two subsets; one set was used to set the rule-based classification and one was used to test the performance of the scheme. Results. In a preliminary clinical study, the implemented detection scheme yielded 98% sensitivity with a false-positive detection rate of less than one false-positive region per image. Conclusion. Single-image segmentation methods seem to have high sensitivity in selecting true-positive mass regions in the first stage of a CAD scheme. A multilayer topographic image feature analysis method in the second stage of a CAD scheme has the potential to significantly reduce the false-positive detection rate.