Dirichlet-Based Concentric Circle Feature Transform for Breast Mass Classification

Breast cancer has caused more and more attention in recent years since the mortality rate is increasing and age of onset is trend to be younger than before. Using computer vision technology for automatic classifying benign and masses malignant ones could assist doctors in diagnosing condition. However, the margins and shapes of masses are various and which are very similar with surrounding tissues, there are still a lot of problems in classification for which the extracted features couldn´t express the original image very well. Hence, in this paper, a new mass feature extraction method is proposed for enhancing the performance of classification. First, the concentric circle bag-of-words (BOW) of the mass is captured spatially from the mammographic images. Then, the Dirichlet fisher kernel is conducted to enhance discrimination ability of the feature. Finally, the SVM classifier is employed for mass classification. Experiments are conducted on DDSM dataset and achieve good classification accuracy.