Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method

This paper introduces a new method of feature extraction from wavelet coefficients for classification of digital mammograms. A matrix is constructed by putting wavelet coefficients of each image of a building set as a row vector. The method consists then on selecting by threshold, the columns which will maximize the Euclidian distances between the different class representatives. The selected columns are then used as features for classification. The method is tested using a set of images provided by the Mammographic Image Analysis Society (MIAS) to classify between normal and abnormal and then between benign and malignant tissues. For both classifications, a high accuracy rate (98%) is achieved.