Mass classification in mammograms based on two-concentric masks and discriminating texton

Abstract

In this paper, a mass classification method in mammograms is proposed based on two-concentric masks and discriminating texton. First, the two-concentric masks are employed, dividing each mass region into the center region and the periphery region. Then integrating linear discriminant analysis (LDA) with traditional texton, the discriminating texton is proposed. The shortage of not considering the class information in traditional texton is improved. Finally, features are extracted with discriminating texton for both the center region and the periphery region. Thus, the problem of disregarding the spatial layout information is alleviated. The proposed method is tested on 130 mass regions from Digital Database for Screening Mammography (DDSM) database. The classification accuracy rate reaches 86.92% and the area under the receiver operating characteristics (ROC) curve is 0.91, which is higher than traditional texton and some other texture-based methods.