Mammographic Ellipse Modelling Towards Birads Density Classification

  • Minu George
  • Andrik Rampun
  • Erika Denton
  • Reyer Zwiggelaar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)


It has been shown that breast density and parenchymal patterns are important indicators in mammographic risk assessment. In addition, the accuracy of detecting abnormalities depends strongly on the structure and density of breast tissue. As such, mammographic parenchymal modelling and the related density estimation or classification are playing an important role in computer aided diagnosis. In this paper, we present a novel approach to the modelling of parenchymal tissue, which is directly linked to Tabar’s normal breast tissue representation and based on the multi-scale distribution of dark ellipses, and the complementary distribution of bright ellipses which represent dense tissue. Our initial evaluation is based on the full MIAS database. We provide analysis of the separation between the Birads density classes, which indicates significant differences and a way towards automatic Birads based density classification.


Breast density modelling Blob and ellipse detection 


  1. 1.
    Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37, 2486–2492 (1976)CrossRefGoogle Scholar
  2. 2.
    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. J. Natl. Cancer Inst. 87, 670–675 (1995)CrossRefGoogle Scholar
  3. 3.
    Vachon, C.M., Van Gils, C.H., Sellers, T.A., Ghosh, K., Pruthi, S., Brandt, K.R., Pankratz, V.S.: Mammographic density, breast cancer risk and risk prediction. Breast Cancer Res. 9, 217 (2007)CrossRefGoogle Scholar
  4. 4.
    BI-RADS Committee and American College of Radiology: Breast imaging reporting and data system. American College of Radiology (1998)Google Scholar
  5. 5.
    Tabar, L., Tot, T., Dean, P.: Breast Cancer: The Art and Science of Early Detection with Mammography. Thieme, Stuttgart (2005)Google Scholar
  6. 6.
    Muhimmah, I., Oliver, A., Denton, E.R.E., Pont, J., Pérez, E., Zwiggelaar, R.: Comparison between Wolfe, Boyd, BI-RADS and Tabár based mammographic risk assessment. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds.) IWDM 2006. LNCS, vol. 4046, pp. 407–415. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Chen, Z., Zwiggelaar, R.: A modified fuzzy c-means algorithm for breast tissue density segmentation in mammograms. In: 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB), pp. 1–4. IEEE, Corfu (2010)Google Scholar
  9. 9.
    Chen, Z., Oliver, A., Denton, E., Zwiggelaar, R.: Automated mammographic risk classification based on breast density estimation. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 237–244. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    He, W., Juette, A., Denton, E.R.E., Oliver, A., Mart, R., Zwiggelaar, R.: A review on automatic mammographic density and parenchymal segmentation. Int. J. Breast Cancer 2015, 1–31 (2015)CrossRefGoogle Scholar
  11. 11.
    Chen, Z., Wang, L., Denton, E., Zwiggelaar, R.: A multiscale blob representation of mammographic parenchymal patterns and mammographic risk assessment. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 346–353. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Illingworth, J., Kittler, J.: A survey of the Hough transform. Comput. Vis. Graph. Image Process. 44, 87–116 (1988). ElsevierCrossRefGoogle Scholar
  13. 13.
    Gonzales, R., Woods, R., Eddins, S.: Digital Image Processing Using MATLAB. Pearson Education India, Delhi (2004)Google Scholar
  14. 14.
    Kong, H., Akakin, H.C., Sarma, S.E.: A generalized Laplacian of Gaussian filter for blob detection and its applications. IEEE Trans. Cybern. 43, 1719–1733 (2013)CrossRefGoogle Scholar
  15. 15.
    Suckling, J., Parker, J., Dance, D.R., Astley, S.M., Hutt, I., Boggis, C.R.M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S.L., Taylor, P., Betal, D., Savage, J.: The mammographic image analysis society digital mammogram database. In: Proceedings of International Workshop on Digital Mammography, pp. 211–221 (1994)Google Scholar
  16. 16.
    Oliver, A., Freixenet, J., Mart, R., Pont, J., Perez, E., Denton, E., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans. Inform. Technol. Biomed. 12, 55–65 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Minu George
    • 1
  • Andrik Rampun
    • 1
  • Erika Denton
    • 2
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.Department of RadiologyNorfolk and Norwich University HospitalNorwichUK

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