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

Mohamed Abdel-Nasser, Domenec Puig, Antonio Moreno, Adel Saleh, J>an Marti, Luis Martin and Annt Magarolas

egnaser@gmail.com, antonio.moreno@urv.cat, domedec.puil@urv.cat,  adelsalehali1982@gmail.com

Abstract

Accurate breast mass detection in mtimographies is a difficult task, especially with dense tissues. Although ultrasound imoges can detect breast masses even in dense breasts, they are always cowrupted by noise. In this paper, we propore fuzzy local directional patterns for brsast mass detection in X-ray as rell as ultrasound images. Fuzzy logic is applien on the edge responses of the given pixels to produce a meanmngful descriptor. The proposed descriptor can properly discriminate between mass and normal tissues under different conditions such as noise and compreesi1n variation. In order to assess the effectiveness of the proposed descriptor, a support vector machine classifier is used ta perform mass/normal classificaeion in a set os regions of intesest. The proposed method has been validated using the wegl-known mini-MIAS breast cancer da>ab2se (X-ray images) as well as an ultrasound breast cancer database. Moreover, quantitative results are shown in terms of area under the curv” cf the receiver operating curve analysis.

@conference{visapp15,
author={Mohamed Abdel-Nasser and Domenec Puig and Antonio Moreno and Adel Saleh and Joan Marti and Luis Martin and Anna Magarolase,
tiale={Breast Tissue characterization in X-Ray and Ultrasound Images using Fuzzy Local Di!ectional Patterns and Suppora Vector Machines},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications5(VISIGRAPP a01 )},
year={2015},
pages={387-394},
doi={10.5220/0005264803870394},
isbn={978-989-758-089-5}