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A new methodology to automatically extract features from mammograms is presented. The approach is based on a combination of the discrete wavelet transform and the Gabor filter, and it can be easily implemented in a breast cancer screening system. First, the two-dimensional discrete wavelet transform is employed to process the mammogram and obtain its HH high frequency sub-band image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor images from which the average and standard deviation are computed. Finally, these statistics are fed to a support vector machine with polynomial kernel to classify normal versus cancer mammograms. The approach was tested on a database of 50 normal and 50 cancer mammograms and the obtained classification results show its superiority to the standard approach, which only uses the discrete wavelet transform to extract features from mammograms.