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15 May 2003 Mammographic mass characterization using sharpness and lobulation measures
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For radiologists lesion margin appearance is of high importance when classifying breast masses as malignant or benign lesions. In this study, we developed different measures to characterize the margin of a lesion. Towards this goal, we developed a series of algorithms to quantify the degree of sharpness and lobulation of a mass margin. Besides, to estimate spiculation of a margin, features previously developed for mass detection were used. Images selected from the publicly available data set "Digital Database for Screening Mammography" were used for development and evaluation of these algorithms. The data set consisted of 777 images corresponding to 382 patients. To extract lesions from the mammograms a segmentation algorithm based on dynamic programming was used. Features were extracted for each lesion. A k-nearest neighbor algorithm was used in combination with a leave-one-out procedure to select the best features for classification purposes. Classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. The average test Az value for the task of classifying masses on a single mammographic view was 0.79. In a case-based evaluation we obtained an Az value of 0.84.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Celia Varela, J. M. Muller, and Nico Karssemeijer "Mammographic mass characterization using sharpness and lobulation measures", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003);

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