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 were 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.