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.