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8 March 2011 Classification of mammographic masses: use and influence of a bilateral-filter-based flat-texture approach
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Abstract
Computer-assisted diagnosis (CADx) for the interactive characterization of mammographic masses as benign or malignant has a high potential to help radiologists during the critical process of diagnostic decision making. By default, the characterization of mammographic masses is performed by extracting features from a region of interest (ROI) depicting the mass. To investigate the influence of a so-called bilateral filter based emph{flat texture} (FT) preprocessing step on the classification performance, textural as well as frequency-based features are calculated in the ROI, in the core of the mass and in the mass margin for preprocessed and unprocessed images. Furthermore. the influence of the parameterization of the bilateral filter on the classification performance is investigated. Additionally, as reference Median and Gaussian filters have been used to compute the FT image and the resulting classification performances of the feature extractors are compared to those obtained with the bilateral filters. Classification is done using a k-NN classifier. The classification performance was evaluated using the area Az under the receiver operating characteristic (ROC) curve. A publicly available mammography database was used as reference image data set. The results show that the proposed FT preprocessing step has a positive influence on the texture-based feature extractors while most of the frequency-based feature extractors perform better on the unprocessed images. For some of the features the original Az could be improved up to 10%. The comparison of the bilateral filter approach with the Median and Gaussian filter approaches showed the superiority of the bilateral filter.
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Florian Wagner, Matthias Elter, Rüdiger Schulz-Wendtland, and Thomas Wittenberg "Classification of mammographic masses: use and influence of a bilateral-filter-based flat-texture approach", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796324 (8 March 2011); https://doi.org/10.1117/12.878004
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