<title>Texture feature extraction methods for microcalcification classification in mammograms</title>

Abstract

We present development, application, and performance evaluation of three different texture feature extraction methods for classification of benign and malignant microcalcifications in mammograms. The steps of the work accomplished are as follows. (1) A total of 103 regions containing microcalcifications were selected from a mammographic database. (2) For each region, texture features were extracted using three approaches: co-occurrence based method of Haralick; wavelet transformations; and multi-wavelet transformations. (3) For each set of texture features, most discriminating features and their optimal weights were found using a real-valued genetic algorithm (GA) and a training set. For each set of features and weights, a KNN classifier and a malignancy criterion were used to generate the corresponding ROC curve. The malignancy of a given sample was defined as the number of malignant neighbors among its K nearest neighbors. The GA found a population with the largest area under the ROC curve. (4) The best results obtained using each set of features were compared. The best set of features generated areas under the ROC curve ranging from 0.82 to 0.91. The multi-wavelet method outperformed the other two methods, and the wavelet features were superior to the Haralick features. Among the multi-wavelet methods, redundant initialization generated superior results compared to non-redundant initialization. For the best method, a true positive fraction larger than 0.85 and a false positive fraction smaller than 0.1 were obtained.