Abstract
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.
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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 Medicine
Boyd, N., Dite, G., Stone, J., et al.: Realiability of Mammographic Density, a Risk Factor for Breast Cancer. New England Journal of Med. 347(12), 886–894 (2002)
Ursin, G., Hovanessian-Larsen, L., Parisky, Y.R., et al.: Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Research 7(5), 605–608 (2005)
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)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37, 2486–2492 (1976)
Oliver, A., Freixenet, J., Martí, R., et al.: A novel breast tissue density classification methodology. IEEE Trans. on Inform. Techn. in Biomed. 12, 55–65 (2008)
Yafee, M., Boyd, N.: Mammographic breast density and cancer risk: The radiological view. Gynecological Endocrinology 21(suppl. 1), 6–11 (2005)
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)
Harvey, J.A., Bovbjerg, V.E.: Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk. Radiology 230(1), 29–41 (2004)
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)
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)
Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532–550 (1987)
Heijden, F., Duin, R., Ridder, D., Tax, D.: Classification, parameter estimation and state estimation - an engineering approach using Matlab. John Wiley & Sons, Chichester (2004)
Kuncheva, L.I.: Combining Pattern Classifiers. John Wiley & Sons, Inc., Chichester (2004)
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)
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Vállez, N., Bueno, G., Déniz, Ó., Esteve, P., Rienda, M.A., Pastor, C. (2010). Automatic Breast Tissue Classification Based on BIRADS Categories. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_35
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DOI: https://doi.org/10.1007/978-3-642-13666-5_35
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