**4. Feature extraction**

Feature extraction plays key role in our breast cancer diagnosis framework. Feature extraction process can only be carried out if the suspicious areas of breast masses are appropriately defined. In selecting effective features from mammogram lesions, great research efforts have been focused on capturing the texture of images and improving correlation to the human visual similarity. Among them, Curvelet transform, Gabor Wavelet, Discrete Wavelet Transform (DWT), and Spherical Wavelet Transform (SWT), Contourlet Transform (CT), local binary pattern (LBP) have been extensively investigated and compared in addition to other popular texture features derived from the co-occurrence matrices and Fourier transformation [39–44]. Since, clinically and visually similar lesions or disease patterns can depict on different locations of the mammograms with different orientations, the selected features should be invariant to the linear shift and rotation of the targeted lesions. To consider these criteria, NSCT and HOG based approaches are used for feature extraction in addition to traditional shape, mass and GLCM based features from region-of-interests (ROIs) with adaptively adjusted size based on the actual mass region segmentation results.
