Skip to main content
SpringerLink
Log in

Breast Density Segmentation Using Texture

  • Conference paper
Digital Mammography (IWDM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4046))

Included in the following conference series:

Abstract

This paper describes an algorithm to segment mammo- graphic images into regions corresponding to different densities. The breast parenchymal segmentation uses information extracted for statistical texture based classification which is in turn incorporated in multi-vector Markov Random Fields. Such segmentation is key to developing quantitative mammographic analysis. The algorithm’s performance is evaluated quantitatively and qualitatively and the results show the feasibility of segmenting different mammographic densities.

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

Access this chapter

Log in via an institution
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
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

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. Heine, J., Malhotra, P.: Mammographic tissue, breast cancer risk, serial image analysis and digital mammography. part 1. tissue and related risk factors. Academic Radioly 9, 298–316 (2002)

    Article  Google Scholar 

  2. Miller, P., Astley, S.: Classification of breast tissue by texture and analysis. Image and Vision Computing 10, 277–282 (1992)

    Article  Google Scholar 

  3. Zwiggelaar, R., Planiol, P., Marti, J., Marti, R., Blot, L., Denton, E., Rubin, C.: EM texture segmentation of mammographic images. In: Peitgen, H. (ed.) International Workshop on Digital Mammography, Bremen, Germany, pp. 223–227. Springer, Heidelberg (2002)

    Google Scholar 

  4. Petroudi, S., Kadir, T., Brady, M.: Automatic classification of mammographic parenchymal patterns: A statistical approach. In: Proceedings of EMBC, International Conference on Engineering in Medicine and Biology, pp. 798–801. IEEE, Los Alamitos (2003)

    Google Scholar 

  5. Brault, P., Mohammad-Djafari, A.: Bayesian segmentation and motion estimation in video sequences using a markov-potts model. In: Proceedings of WSEAS Conference on Applied Mathematics 2004 (2004)

    Google Scholar 

  6. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation maximization algorithm. IEEE Trans. Med. Imag. 20(1), 45–57 (2001)

    Article  Google Scholar 

  7. Varma, M., Zisserman, A.: Classifying images of materials: Achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  8. Fwu, J.K., Djuric, P.M.: Unsupervised vector image segmentation by a tree structure-icm algorithm. IEEE Trans. Med. Imag. 15(6), 871–880 (1996)

    Article  Google Scholar 

  9. Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B (Methodological) 48(3), 259–302 (1986)

    MATH  MathSciNet  Google Scholar 

  10. Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B 36(2), 192–236 (1974)

    MATH  MathSciNet  Google Scholar 

  11. Linguraru, M.G., Marias, K., Brady, J.M.: Temporal mass detection. In: International Workshop on Digital Mammography (2002)

    Google Scholar 

  12. Petroudi, S., Brady, M.: Breast segmentation. In: Seventh International Workshop on Digital Mammography. Medical Physics Publishing (2004)

    Google Scholar 

  13. Wolfe, J.N.: Breast parenchymal patterns and their changes with age. Radiology 121, 545–552 (1976)

    Google Scholar 

  14. Vujovic, N., Brzakovic, D.: Establishing correspondence between control points in pairs of mammographic images. IEEE Transactions on Image Processing 6(10), 1388–1399 (1997)

    Article  Google Scholar 

  15. Evertsz, C.J.G., Bodicker, A., Bohnenkamp, S., Dechow, D., Beck, C., Peitgen, H.-O., Berger, L., Weber, U., Jurgens, H., Hendriks, C.L., Karssemeijer, N., Brady, M.: Soft-copy reading environment for screening mammography - screen. In: Yaffe, M. (ed.) Fifth International Workshop in Digital Mammogrphy, pp. 566–572 (2000)

    Google Scholar 

  16. Miller, P., Astley, S.: Automated detection of mammographic asymmetry using anatomical features. International Journal of Pattern Recognition and Artificial Intelligence 7(6), 1461–1476 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wolfson Medical Vision Laboratory, Oxford University, Oxford, OX2 7DD, United Kingdom

    Styliani Petroudi & Michael Brady

Authors
  1. Styliani Petroudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Brady
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Division of Imaging Science and Biomedical Engineering, Stopford Building, University of Manchester, Oxford Road, M13 9PT, Manchester

    Susan M. Astley

  2. Wolfson Medical Vision Lab, University of Oxford, UK

    Michael Brady

  3. Imaging Science and Biomedical Engineeering, University of Manchester, Oxford Road, M13 9PT, Manchester, UK

    Chris Rose

  4. Department of Computer Science, University of Wales, Aberystwyth, UK

    Reyer Zwiggelaar

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Petroudi, S., Brady, M. (2006). Breast Density Segmentation Using Texture. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_82

Download citation

  • DOI: https://doi.org/10.1007/11783237_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35625-7

  • Online ISBN: 978-3-540-35627-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation