Advertisement

Mammographic Segmentation and Risk Classification Using a Novel Binary Model Based Bayes Classifier

  • Wenda He
  • Erika R. E. Denton
  • Reyer Zwiggelaar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7361)

Abstract

Clinical research has shown that the sensitivity of mammography is significantly reduced by increased breast density, which can mask some tumours due to dense fibroglandular tissue. In addition, there is a clear correlation between the overall breast density and mammographic risk. We present an automatic mammographic density segmentation approach using a novel binary model based Bayes classifier. The Mammographic Image Analysis Society (MIAS) database was used in a quantitative and qualitative evaluation. Visual assessment on the segmentation results indicated a good and consistent extraction of mammographic density. With respect to mammographic risk classification, substantial agreements were found between the classification results and ground truth provided by expert screening radiologists. Classification accuracies were 85% and 78% in Tabár and Breast Imaging Reporting and Data System (Birads) categories, respectively; whilst in the corresponding low and high categories, the classification accuracies were 93% and 88% for Tabár and Birads, respectively.

Keywords

Mammographic Density Breast Density Binary Model Percentage Mammographic Density Mammographic Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Office for National Statistics. Cancer statistics registrations: Registrations of cancer diagnosed in 2007, england. MB1(38) (2010)Google Scholar
  2. 2.
    Bray, F., McCarron, P., Parkin, D.M.: The changing global patterns of female breast cancer incidence and mortality. Breast Cancer Research 6(6), 229–239 (2004)CrossRefGoogle Scholar
  3. 3.
    Tabár, L., Tot, T., Dean, P.B.: Breast Cancer: The Art And Science Of Early Detection With Mamography: Perception, Interpretation, Histopatholigic Correlation, 1st edn., December 16. Georg Thieme Verlag (2004)Google Scholar
  4. 4.
    Wolfe, J.N.: Risk for breast cancer development determind by mammographic parenchymal pattern. Cancer 37(5), 2486–2492 (1976)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    American College of Radiology. Breast Imaging Reporting and Data System BI-RADS, 4th edn. American College of Radiology, Reston (2004)Google Scholar
  7. 7.
    Sickles, E.A.: Wolfe mammographic parenchymal patterns and breast cancer risk. American Journal of Roentgenology 188(2), 301–303 (2007)CrossRefGoogle Scholar
  8. 8.
    Aylward, S.R., Hemminger, B.M., Pisano, E.D.: Mixture modeling for digital mammogram display and analysis. In: The 4th International Workshop on Digital Mammography, pp. 305–312. Kulwer Academic Publishers (1998)Google Scholar
  9. 9.
    Ferrari, R.J., Rangayyan, R.M., Borges, R.A., Frère, A.F.: Segmentation of the fibro-glandular disc in mammograms using gaussian mixture modelling. Medical & Biological Engineering & Computing 42(3), 378–387 (2004)CrossRefGoogle Scholar
  10. 10.
    Selvan, S.E., Xavier, C.C., Karssemeijer, N., Sequeira, J., Cherian, R.A., Dhala, B.Y.: Parameter estimation in stochastic mammogram model by heuristic optimization techniques. IEEE Transactions on Information Technology in Biomedicine, 685–695 (2006)Google Scholar
  11. 11.
    Highnam, R., Brady, M.: Mammographic Image Analysis. Kluwer Academic Publishers, London (1999)zbMATHCrossRefGoogle Scholar
  12. 12.
    Oliver, A., Freixenet, J., Zwiggelaar, R.: Automatic classification of breast density. In: Proceedings of the 2005 International Conference on Image Processing, vol. 2, pp. 1258–1261 (2005)Google Scholar
  13. 13.
    Zwiggelaar, R., Denton, E.R.E.: Mammographic risk assessment and local greylevel appearance histograms. In: 10th International Conference on Information Technology and Applications in Biomedicine, p. 1 (2010)Google Scholar
  14. 14.
    Marias, K., Petroudi, S., English, R., Adams, R., Brady, M.: Subjective and computer-based characterisation ofmammographic patterns. In: The 6th International Workshop on Digital Mammography, pp. 552–556 (2002)Google Scholar
  15. 15.
    Petroudi, S., Marias, K., English, R., Brady, M.: Classification of mammogram patterns using area measurements and the standard mammogram form (smf). In: Medical Image Analysis and Understanding, pp. 197–200 (2002)Google Scholar
  16. 16.
    Petroudi, S., Kadir, T., Brady, M.: Automatic classification of mammographic parenchymal patterns: A statistical approach. In: Engineering in Medicine and Biology Society, vol. 1, pp. 798–801 (2003)Google Scholar
  17. 17.
    Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E.R.E., Zwiggelaar, R.: A novel breast tissue density classification framework. Information Technology in BioMedicine 12, 55–65 (2008)CrossRefGoogle Scholar
  18. 18.
    Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., Taylor, P., Betal, D., Savage, J.: The mammographic images analysis society digital mammogram database. In: Dance, Gale, Astley, Gairns (eds.) Excerpta Medica. International Congress Series, vol. 1069, pp. 375–378. Elsevier (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wenda He
    • 1
  • Erika R. E. Denton
    • 2
  • Reyer Zwiggelaar
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.Department of RadiologyNorfolk & Norwich University HospitalNorwichUK

Personalised recommendations