Improving the detection of simulated masses in mammograms through two different image-processing techniques

Rationale and Objectives. The purpose of this study was to determine whethe r contrast-limited adaptive histogram equalization (CLAHE) or histogram-bas ed intensity windowing (HIW) improves the detection of simulated masses in dense mammograms. Materials and Methods. Simulated masses were embedded in portions of mammog rams of patients with dense breasts; the mammograms were digitized at 50 mu m per pixel, 12 bits deep. In two different experiments, images were printe d both with no processing applied and with related parameter settings of tw o image-processing methods. A simulated mass was embedded in a realistic ba ckground of dense breast tissue, with its position varied. The key variable s in each trial included the position of the mass, the contrast levels of t he mass relative to the background, and the selected parameter settings for the image-processing method. Results. The success in detecting simulated masses on mammograms with dense backgrounds depended on the parameter settings of the algorithms used. The best HIW setting performed better than the best fixed-intensity window set ting and better than no processing. Performance with the best CLAHE setting s was no different from that with no processing. In the HIW experiment, the re were no significant differences in observer performance between processi ng conditions for radiologists and nonradiologists. Conclusion. HIW should be tested in clinical images to determine whether th e detection of masses by radiologists can be improved. CLAHE processing wil l probably not improve the detection of masses on clinical mammograms.