@inproceedings{scholars594,
           pages = {318--322},
          volume = {2},
         journal = {2009 International Conference on Computer and Electrical Engineering, ICCEE 2009},
         address = {Dubai},
            note = {cited By 20; Conference of 2009 International Conference on Computer and Electrical Engineering, ICCEE 2009 ; Conference Date: 28 December 2009 Through 30 December 2009; Conference Code:79725},
           title = {Digital mammograms classification using a wavelet based feature extraction method},
            year = {2009},
             doi = {10.1109/ICCEE.2009.39},
        keywords = {Accuracy rate; Breast Cancer; Component; Digital mammograms; Euclidian distance; Malignant tissues; Mammographic image analysis; matrix; Wavelet coefficients; Wavelet-based Feature, Electrical engineering; Image analysis; Mammography; Wavelet analysis; Wavelet transforms; X ray screens, Feature extraction},
            isbn = {9780769539256},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950838117&doi=10.1109\%2fICCEE.2009.39&partnerID=40&md5=de061181f824b607c4d371ee40404f9e},
          author = {Faye, I. and Samir, B. B. and Eltoukhy, M. M. M.},
        abstract = {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. {\^A}{\copyright} 2009 IEEE.}
}