Mammographic mass characterization using sharpness and lobulation measures

Celia Varela; J. M. Muller; Nico Karssemeijer

doi:10.1117/12.480161

15 May 2003 Mammographic mass characterization using sharpness and lobulation measures

Celia Varela, J. M. Muller, Nico Karssemeijer

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480161
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

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 A_z under the receiver operating characteristic curve. The average test A_z value for the task of classifying masses on a single mammographic view was 0.79. In a case-based evaluation we obtained an A_z value of 0.84.

Citation Download Citation

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); https://doi.org/10.1117/12.480161

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