Automatic breast density segmentation based on pixel classification

Michiel G. J. Kallenberg; Mariëtte A. J. Lokate; Carla H. van Gils; Nico Karssemeijer

doi:10.1117/12.877522

4 March 2011 Automatic breast density segmentation based on pixel classification

Michiel G. J. Kallenberg, Mariëtte A. J. Lokate, Carla H. van Gils, Nico Karssemeijer

Proceedings Volume 7963, Medical Imaging 2011: Computer-Aided Diagnosis; 796307 (2011) https://doi.org/10.1117/12.877522
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Mammographic breast density has been found to be a strong risk factor for breast cancer. In most studies it is assessed with a user assisted threshold method, which is time consuming and subjective. In this study we develop a breast density segmentation method that is fully automatic. The method is based on pixel classification in which different approaches known in literature to segment breast density are integrated and extended. In addition the method incorporates knowledge of a trained observer, by using segmentations obtained by the user assisted threshold method as training data. The method is trained and tested using 1300 digitised film mammographic images acquired with a variety of systems. Results show a high correspondence between the automated method and the user assisted threshold method. The Spearman's rank correlation coefficient between our method and the user assisted method was R = 0.914 for percent density, which is substantially higher than the best correlation found in literature (R=0.70). The AUC obtained when discriminating between fatty and dense pixels was 0.985. A combination of segmentation strategies outperformed the application of a single segmentation technique. The method was shown to be robust for differences in mammography systems, image acquisition techniques and image quality.

Citation Download Citation

Michiel G. J. Kallenberg, Mariëtte A. J. Lokate, Carla H. van Gils, and Nico Karssemeijer "Automatic breast density segmentation based on pixel classification", Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796307 (4 March 2011); https://doi.org/10.1117/12.877522

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