Multi-resolution morphological analysis and classification of mammographic masses using shape, spectral and wavelet features

This study constitutes a comprehensive signal analysis approach to the morphological characterization of mammographic mass shape. Three distinct areas of shape morphology ware exploited for feature extraction. Specifically, the radial distance signal, the DFT spectrum envelope and the DWT decomposition with multiple wavelet function choices, were analyzed by seven curve feature functions, as carriers of significant discriminating information. Classification was conducted against the morphological shape type identification, as well as the verified clinical diagnosis, using optimized feature set selections and combinations by multivariate statistical significance analysis. All available datasets and configurations were applied to a wide range of linear and neural classifiers, including linear discriminant analysis, least-squares minimum distance, K-nearest neighbor, RBF and MLP neural networks, Neural classifiers outperformed linear equivalents in all cases, producing an overall accuracy of 72.3% for morphological shape type identification and 89.2% for clinical diagnosis identification

Mammographic mass classification using textural features and descriptive diagnostic data

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