Semivariogram applied for classification of benign and malignant tissues in mammography

Abstract

This work analyzes the application of the semivariogram function to the characterization of breast tissue as malignant or benign in mammographic images. The method characterization is based on a process that selects, using stepwise technique, from all computed semivariance which best discriminate between the benign and malignant tissues. Then, a multilayer perceptron neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 117 tissues samples, 67 benign and 50 malignant. The result analysis has given a sensitivity of 92.8%, a specificity of 83.3% and an accuracy above 88.0%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue. © Springer-Verlag Berlin Heidelberg 2006.