关键词: 非下采样轮廓变换（NSCT）, 泽尼克矩, 乳腺癌, 乳腺X线图像, 支持向量机（SVM）

Abstract:  Breast cancer is the most common cancer among women. With the development of medical treatment, the diagnosis and treatment techniques to breast cancer have improved, but it is difficult to make a timely diagnosis in the early stages of breast cancer, which leads the mortality of breast cancer still high. In view of this phenomenon, this paper studies a classification method of mammography based on nonsubsampled contourlet transform (NSCT). The method firstly decomposes the region of interest (ROI) of mammography based on the multiresolution NSCT, then uses the Zernike moments (Z-Moments) to extract the features of the image which is decomposed by the NSCT. Secondly, for each feature matrix extracted from the ROI, this method decomposes the feature matrix to get the important features which can be extracted to summarize the global characteristics through the singular value decomposition (SVD). This method combines both texture and shape features, and uses the support vector machines (SVM) classification algorithm to define the mammogram images as normal, benign and malignant, achieves the detection and classification of breast lesions. The experimental results show that the accuracy rate reaches 96.76%, and training time greatly reduces. Compared with the state-of-the-art methods, marked improvements are achieved in both accuracy and time efficiency.

Key words: nonsubsampled contourlet transform (NSCT), Zernike moments, breast cancer, mammography, support vector machines (SVM)