Schebesch F, Unberath M, Andersen I, Maier A (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Kluwer Academic Publishers
Pages Range: 38-43
Conference Proceedings Title: Informatik aktuell
Event location: Berlin, DEU
ISBN: 9783662494646
DOI: 10.1007/978-3-662-49465-3_9
Breast density differs from almost entirely fatty to extremely dense tissue composition. In mammography screenings, physicians are often supported by computer-aided detection and diagnosis systems (CAD) whose detection rate is affected by the density of the breast. An automatic pre-assessment of breast density would enable a specific analysis adapted to each density class. Digital mammograms from the INbreast database [1] are decomposed into Haar-Wavelet components and several levels are used for classification. A random forest classifier is applied on the averaged Wavelet components for four class densities which yields an accuracy of 64.53% in CC-view and 51.22% in MLO-view. The 3-class problem with a combined class of medium densities yields an accuracy of 73.89% in CC-view and 67.80% in MLO-view.
APA:
Schebesch, F., Unberath, M., Andersen, I., & Maier, A. (2017). Breast density assessment using wavelet features on mammograms. In Thomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer (Eds.), Informatik aktuell (pp. 38-43). Berlin, DEU: Kluwer Academic Publishers.
MLA:
Schebesch, Frank, et al. "Breast density assessment using wavelet features on mammograms." Proceedings of the Workshops on Image processing for the medicine, 2016, Berlin, DEU Ed. Thomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer, Kluwer Academic Publishers, 2017. 38-43.
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