Automatic Breast Tissue Classification Based on BIRADS Categories
Breast tissue density is an important risk factor in the detection of breast cancer. It is also known that interpretation of mammogram lesions is more difficult in dense tissues. Therefore, getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis. This article reviews several classification techniques for two datasets, both digitized screen-film (SFM) and full-field digital (FFDM) mammography, classified according to BIRADS categories. It concludes with a hierarchical classification procedure based on k-NN combined with principal component analysis on texture features. The results obtained classifying 1740 mammograms reflect up to 83% of samples correctly classified. The method is being integrated within a CADe system developed by the authors.
KeywordsBreast Cancer Principal Component Anal Support Vector Machine Breast Tissue Mammographic Density
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- 1.Bueno, G.: 10. In: Fuzzy Systems and Deformable Models. Series in Medical Physics and Biomedical Engineering, pp. 305–329. Taylor & Francis Group, London (2008); Book-Title: Intelligent and Adaptive Systems in MedicineGoogle Scholar
- 4.Bueno, G., Ruiz, M., Sánchez, S.: B-spline filtering for automatic detection of calcification lesions in mammograms. In: Proceedings of the Intern. Conference on Information Optics, WIO 2006, pp. 60–70 (2006)Google Scholar
- 8.Brem, R., Hoffmeister, J., Rapelyea, J., et al.: Impact of breast density on computer-aided detection for breast cancer. American Journal of Roentgenology 184, 439–444 (2005)Google Scholar
- 10.Bovis, K., Singh, S.: Classification of mammographic breast density using a combined classifier paradigm. In: 4th Intern. Workshop on Digital Mammography, pp. 177–180 (2002)Google Scholar
- 11.Oliver, A., Lladó, X., Martí, R., Freixenet, J., Zwiggelaar, R.: Classifying mammograms using texture information. In: Proc. Medical Image Understanding and Analysis, July 2007, pp. 223–227 (2007)Google Scholar
- 15.Petroudi, S., Kadir, T., Brady, M.: Automatic classification of mammographic parenchymal patterns: A statistical approach. In: Proc. IEEE Conf. Eng. Med. Biol. Soc., vol. 1, pp. 798–801 (2003)Google Scholar