Mammographic mass classification using textural features and descriptive diagnostic data

Abstract

Texture analysis is one of the most important factors in breast tissue characterization. An analytical approach to texture classification, combined with qualitative descriptive diagnostic data, is presented in this article. For qualitative data, a statistical approach was applied in detailed clinical findings and texture-related features were established as of most importance during the diagnostic assertion process. A complete set of textural feature functions in multiple configurations and implementations was applied to a large set of digitized mammograms, in order to establish the discriminating value and statistical correlation with qualitative texture descriptions of breast mass tissue. Multiple linear and non-linear models were applied during the classification process, including LDA, least-squares minimum distance, K-nearest-neighbors, RBF and MLP. Optimal classification accuracy rates reached 81.5% for texture-only classification and 85.4% with the introduction of patient's age as an example of hybrid approaches.