Abstract
Mammographic tissue density is considered to be one of the major risk factors for developing breast cancer. In this paper we use quantitative measurements of Local Binary Patterns and its variants for breast tissue classification. We compare the classification results of LBP, ELBP, Uniform ELBP and M-ELBP for classifying mammograms as fatty, glandular and dense. A Bayesian-Network classifier is used with stratified ten-fold cross-validation. The experimental results indicate that ELBP patterns at different orientations extract more relevant elliptical breast tissue information from the mammograms indicating the importance of directional filters for breast tissue classification.
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Oliver, A., et al.: A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12, 55–65 (2008)
McCormack, V.A., dos Santos Silva, I.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Prev. Biomark. 15(6), 1159–1169 (2006)
George, M., Rampun, A., Denton, E., Zwiggelaar, R.: Mammographic ellipse modelling towards birads density classification. In: Tingberg, A., Lång, K., Timberg, P. (eds.) IWDM 2016. LNCS, vol. 9699, pp. 423–430. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41546-8_53
Obenauer, S., Sohns, C., Werner, C., Grabbe, E.: Impact of breast density on computer-aided detection in full-field digital mammography. J. Digit. Imaging 19(3), 258 (2006)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)
Kallenberg, M.G., Lokate, M., Van Gils, C.H., Karssemeijer, N.: Automatic breast density segmentation: an integration of different approaches. Phys. Med. Biol. 56(9), 2715 (2011)
Muhimmah, I., Zwiggelaar, R.: Mammographic density classification using multiresolution histogram information. In: Proceedings of the International Special Topic Conference on Information Technology in Biomedicine, ITAB, October 2006
Zwiggelaar, R., Muhimmah, I., Denton, E.R.E.: Mammographic density classification based on statistical grey-level histogram modeling. In: Proceedings of the Medical Image Understanding and Analysis (MIUA 2005), pp. 183–186 (2005)
Hadjidemetriou, E., Grossberg, M.D., Nayar, S.K.: Multiresolution histograms and their use for recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 831–847 (2004)
Zhou, C., et al.: Computerized image analysis: estimation of breast density on mammograms. Med. Phys. 28(6), 1056–1069 (2001)
He, W., Denton, E.R.E., Zwiggelaar, R.: Mammographic segmentation and risk classification using a novel binary model based Bayes classifier. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 40–47. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31271-7_6
Oliver, A., Freixenet, J., Zwiggelaar, R.: Automatic classification of breast density. In: IEEE International Conference on Image Processing, 2005. ICIP 2005, vol. 2, pp. II-1258. IEEE (2005)
Mutra, M., Grgi, M., Dela, K.: Breast density classification using multiple feature selection. automatika 53(4), 362–372 (2012)
Petroudi, S., Brady, M.: Breast density segmentation using texture. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 609–615. Springer, Heidelberg (2006). https://doi.org/10.1007/11783237_82
Oliver, A., Llad, X., Marti, R., Freixenet, J., Zwiggelaar, R.: Classifying mammograms using texture information. In: Medical Image Understanding and Analysis, Vol. 223 (2007)
Nanni, L., Lumini, A., Brahnam, S.: Local binary patterns variants as texture descriptors for medical image analysis. Artif. Intell. Med. 49(2), 117–125 (2010)
Nguyen, H.-T., Caplier, A.: Elliptical local binary patterns for face recognition. In: Park, J.-I., Kim, J. (eds.) ACCV 2012. LNCS, vol. 7728, pp. 85–96. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37410-4_8
Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45054-8_27
Suckling, J., et al.: The mammographic image analysis society digital mammogram database. In: International Congress Series Exerpta Medica, vol. 1069, pp. 375–378 (1994)
Ferlay, J., et al.: Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136(5), E359–E386 (2015)
American College of Radiology, Illustrated Breast Imaging Reporting and Data System BIRADS. American College of Radiology, PA, Philadelphia (1998)
George, M., Denton, E., Zwiggelaar, R.: Mammogram breast density classification using mean-elliptical local binary patterns. In: International Workshop on Breast Imaging (2018)
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George, M., Zwiggelaar, R. (2018). Breast Tissue Classification Using Local Binary Pattern Variants: A Comparative Study. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_15
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DOI: https://doi.org/10.1007/978-3-319-95921-4_15
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