Wagner, F.F.WagnerWittenberg, T.T.WittenbergElter, M.M.Elter2022-03-112022-03-112010https://publica.fraunhofer.de/handle/publica/36680410.1117/12.844675Computer-assisted diagnosis (CADx) for the characterization of mammographic masses as benign or malignant has a very 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 the region on the classification performance, textural, morphological, frequency- as well as moment-based features are calculated in subregions of the ROI, which has been delineated manually by an expert. The investigated subregions are (a) the semi-automatically segmented area which includes only the core of the mass, (b) the outer border region of the mass, and (c) the combination of the outer and the inner border region, referred to as mass margin. To extract the border region and the margin of a mass an extended version of the rubber band straightening transform (RBST) was developed. Furthermore, the effectiveness of the features extracted from the RBST transformed border region and mass margin is compared to the effectiveness of the same features extracted from the untransformed regions. After the feature extraction process a preferably optimal feature subset is selected for each feature extractor. Classification is done using a k-NN classifier. The classification performance was evaluated using the area Az under the receiver operating characteristic curve. A publicly available mammography database was used as data set. Results showed that the manually drawn ROI lead to superior classification performances for the morphological feature extractors and that the transformed outer border region and the mass margin are not suitable for moment-based features but yield to promising results for textural and frequency-based features. Beyond that the mass margin, which combines the inner and the outer border region, leads to better classification performances compared to the outer border region for its own.en621535conference paper