Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms

We present an evaluation and comparison of the performance of four di5erent texture and shape feature extraction methods for classi(cation of benign and malignant microcalci(cations in mammograms. For 103 regions containing microcalci(cation clusters, texture and shape features were extracted using four approaches: conventional shape quanti(ers; co-occurrence-based method of Haralick; wavelet transformations; and multi-wavelet transformations. For each set of features, most discriminating features and their optimal weights were found using real-valued and binary genetic algorithms (GA) utilizing a k-nearest-neighbor classi(er and a malignancy criterion for generating ROC curves for measuring the performance. The best set of features generated areas under the ROC curve ranging from 0.84 to 0.89 when using real-valued GA and from 0.83 to 0.88 when using binary GA. The multi-wavelet method outperformed the other three methods, and the conventional shape features were superior to the wavelet and Haralick features. ? 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.