Skip to main content

Automatic Breast Tissue Classification Based on BIRADS Categories

  • Conference paper
Digital Mammography (IWDM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6136))

Included in the following conference series:

  • 1494 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

  5. Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37, 2486–2492 (1976)

    Article  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Yafee, M., Boyd, N.: Mammographic breast density and cancer risk: The radiological view. Gynecological Endocrinology 21(suppl. 1), 6–11 (2005)

    Article  Google Scholar 

  8. 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)

    Google Scholar 

  9. Harvey, J.A., Bovbjerg, V.E.: Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk. Radiology 230(1), 29–41 (2004)

    Article  Google Scholar 

  10. 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)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532–550 (1987)

    Article  Google Scholar 

  13. Heijden, F., Duin, R., Ridder, D., Tax, D.: Classification, parameter estimation and state estimation - an engineering approach using Matlab. John Wiley & Sons, Chichester (2004)

    Book  MATH  Google Scholar 

  14. Kuncheva, L.I.: Combining Pattern Classifiers. John Wiley & Sons, Inc., Chichester (2004)

    Book  MATH  Google Scholar 

  15. 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)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13666-5_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13665-8

  • Online ISBN: 978-3-642-13666-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics