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Paper   IPM / Cognitive / 7853
School of Cognitive Sciences
  Title:   Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms
1.  H. Soltanian Zadeh
2.  F. Rafiee Rad
3.  S. Pourabdollah-Nejad
  Status:   Published
  Vol.:  37
  Year:  2004
  Pages:   1973�??1986
  Supported by:  IPM
We present an evaluation and comparison of the performance of four di5erent texture and shape feature extraction methods for classification of benign and malignant microcalcifications in mammograms. For 103 regions containing microcalcification clusters, texture and shape features were extracted using four approaches: conventional shape quantifiers; 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 classifier 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.

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