Breast Tissue Characterization in X-Ray and Ultrasound Images using Fuzzy Local Directional Patterns and Support Vector Machines

Accurate breast mass detection in mammographies is a difficult task, especially with dense tissues. Although ultrasound images can detect breast masses even in dense breasts, they are always corrupted by noise. In this paper, we propose fuzzy local directional patterns for breast mass detection in X-ray as well as ultrasound images. Fuzzy logic is applied on the edge responses of the given pixels to produce a meaningful descriptor. The proposed descriptor can properly discriminate between mass and normal tissues under different conditions such as noise and compression variation. In order to assess the effectiveness of the proposed descriptor, a support vector machine classifier is used to perform mass/normal classification in a set of regions of interest. The proposed method has been validated using the well-known mini-MIAS breast cancer database (X-ray images) as well as an ultrasound breast cancer database. Moreover, quantitative results are shown in terms of area under the curve of the receiver operating curve analysis.