Abstract
We investigated the associations between Tabár based breast parenchymal patterns and Birads density parenchymal patterns in digital mammography. Breast parenchymal texture was analysed on a set of mammographc images segmented based on Tabár tissue modelling. Visual assessment indicates good and anatomically improved segmentation on tissue specific areas. At the tissue modelling stage, over and/or under training can cause tissue composition fluctuation between nodular and homogeneous tissue, whilst the percentages of radiolucent tissue are less sensitive to the algorithm’s parameter configurations. The clear depiction with digital mammography allows a better segmentation on tissue specific areas, which indicates that the breast parenchymal texture may be utilised in mammographic interpretation as the new technology advances further. The average tissue compositions for the Tabár parenchymal patterns show inadequate compositions of nodular and homogeneous tissue. Stronger associates were found between Tabár tissue compositions for [nodular, homogeneous], [nodular, homogeneous, radiolucent] and Birads breast density classes I and IV.
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References
Gillbert, F.J., Young, K.C., Astley, S.M., Whelehan, P., Gillan, M.G.C.: Digital Breast Tomosynthesis. NHSBSP Publication No 69 (September 2010) ISBN 978-1-84463-071-4
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)
Tabár, L., Tot, T., Dean, P.B.: Breast Cancer: The Art And Science Of Early Detection With Mammography: Perception, Interpretation, Histopatholigic Correlation, 1st edn. Georg Thieme Verlag (December 2004)
Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)
Boyd, N.F., Byng, J.W., Jong, R.A., Fishell, E.K., Little, L.E., Miller, A.B., Lockwood, G.A., Tritchler, D.L., Yaffe, M.J.: Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian national breast screening study. Journal of the National Cancer Institute 87, 670–675 (1995)
American College of Radiology. Breast Imaging Reporting and Data System BIRADS, 4th edn. American College of Radiology, Reston (2004)
Boyd, N.F., Martin, L.J., Yaffe, M.J., Minkin, S.: Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Research 13(6), 223–235 (2011)
Sickles, E.A.: Wolfe mammographic parenchymal patterns and breast cancer risk. American Journal of Roentgenology 188(2), 301–303 (2007)
Baker, J.A., Rosen, E.L., Crockett, M.M., Lo, J.Y.: Accuracy of segmentation of a commercial computer-aided detection system for mammography. Radiology 235(2), 385–390 (2005)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ursin, G., Astrahan, M.A., Salane, M., Parisky, Y.R., Pearce, J.G., Daniels, J.R., Pike, M.C., Spicer, D.V.: The detection of changes in mammographic densities. Cancer Epidemiol Biomarkers Prev 7, 43–47 (1998)
Gram, I.T., Bremnes, Y., Ursin, G., Maskarinec, G., Bjurstam, N., Lund, E.: Percentage density, Wolfe’s and Tabár’s mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Research 7(5), R61–R845 (2005)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)
He, W., Denton, E.R.E.: Mammographic Parenchymal Pattern Segmentation: A Clinical Evaluation. In: 10th IEEE International Conference on Information Technology and Applications in Biomedicine, pp. 1–4 (2010)
Lobbes, M.B.I., Cleutjens, J.P.M., Lima Passos, V., Frotscher, C., Lahaye, M.J., Keymeulen, K.B.M.I., Beets-Tan, R.G., Wildberger, J., Boetes, C.: Density is in the eye of the beholder: visual versus semi-automated assessment of breast density on standard mammograms. Insights into Imaging 3(1), 91–99 (2012)
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He, W., Zwiggelaar, R. (2013). Breast Parenchymal Pattern Analysis in Digital Mammography: Associations between Tabár and Birads Tissue Compositions. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_48
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DOI: https://doi.org/10.1007/978-3-642-40246-3_48
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