Computer-Aided Detection of Mammographic Masses Using Hybrid Region Growing Controlled by Multilevel Thresholding

Abstract

Masses are one of the common signs of nonpalpable breast cancer visible in mammograms. However, due to its irregular and obscured margin, variability in size, and occlusion within dense breast tissue, a mass may be missed during screening. In this paper, we propose a novel approach for automatic detection of mammographic masses using an iterative method of multilevel high-to-low intensity thresholding, followed by region growing and reduction of false positives, in which an image is considered as a 3D topographic map with intensity as the third dimension. At each iteration, first, the focal regions of masses are obtained by thresholding, and then potential sites of masses are extracted from the focal regions with a newly developed region growing technique. Finally, false positives are reduced using contrast and distance between two potential mass regions, and by using a classifier after the extraction of shape- and orientation-based features. The performance of the method is evaluated with 120 scanned-film images, including 55 images with 57 masses and 65 normal images from the mini-MIAS database; 555 scanned-film images, including 355 images with 370 masses and 200 normal images from the DDSM; and 219 digital radiography (DR) images, including 99 images with 120 masses and 120 normal images from a local database. For the mini-MIAS, DDSM, and DR images 90% sensitivity is achieved at a rate of 4.4, 0.99, and 1.0 false positive per images, respectively.