Skip to main content

Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm

  • Conference paper
Advanced Concepts for Intelligent Vision Systems (ACIVS 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5259))

Abstract

Breast cancer is the most common cause of death among women and the most effective method for its diagnosis is mammography. However, this kind of analysis is very difficult to interpret and for this reason radiologists miss 20-30% of tumors. We propose a module for the segmentation of masses that can be implemented in a complete CADx (Computer Aided Diagnosis) system. In particular, we implement a new version of the region growing algorithm specific for this kind of images and for the constraints on computation time of this application.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Bankman, I.N., Nizialek, T., Simon, I., Gatewood, O.B., Weinberg, I.N., Brody, W.R.: Algorithms for segmenting small low-contrast objects in images. In: Recent advances in breast imaging, mammography, and computer-aided diagnosis of breast cancer, pp. 723–738. SPIE Press, Bellingham (2006)

    Chapter  Google Scholar 

  2. Correia, P., Pereira, F.: Objective evaluation of relative segmentation quality. In: Proceedings of 2000 International Conference on Image Processing, 2000, vol. 1, pp. 308–311 (2000)

    Google Scholar 

  3. Dinnes, J., Moss, S., Melia, J., et al.: Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: Findings of a systematic review. The Breast 10, 455–463 (2001)

    Article  Google Scholar 

  4. Heath, M., Bowyer, K.W., Kopans, D., Moore, R., Kegelmeyer, P.: Current status of the Digital Database for Screening Mammography. In: Digital Mammography, pp. 457–460. Kluwer Academic Publishers, Dordrecht (1998)

    Chapter  Google Scholar 

  5. Kobatake, H., Murakami, M., Takeo, H., Nawano, S.: Computerized detection of malignant tumors on digital mammograms. IEEE Transactions on Medical Imaging 18(5), 369–378 (1999)

    Article  Google Scholar 

  6. Li, H.D., Kallergi, M., Clarke, M., Jain, L.P., Clark, V.K.: Markov random field for tumor detection in digital mammography. IEEE Transactions on Medical Imaging 14(3), 565–576 (1995)

    Article  Google Scholar 

  7. Matsubara, T., Fujita, H., Endo, T., et al.: Development of mass detection algorithm based on adaptive thresholding technique in digital mammograms. In: Proceedings of the 3rd International Workshop on Digital Mammography, pp. 391–396. Elsevier Science, Amsterdam (1996)

    Google Scholar 

  8. Mencattini, A., Salmeri, M., Lojacono, R., Frigerio, M., Caselli, F.: Mammographic images enhancement and denoising for detection of tumoral signs using dyadic wavelet processing. IEEE Transactions on Instrumentation and Measurement 57(5) (in press)

    Google Scholar 

  9. Mencattini, A., Salmeri, M., Lojacono, R., Rabottino, G., Romano, S.: Mammographic image analysis for tumoral mass automatic classification. In: EFOMP European Conference on Medical Physics (EFOMP 2007), Castelvecchio Pascoli, Italy (September 2007)

    Google Scholar 

  10. Polakowski, W.E., Cournoyer, D.A., Rogers, S.K., et al.: Computer-aided reast cancer detection and diagnosis of masses using difference of gaussians and derivative-based feature saliency. IEEE Trans. Med. Imaging 16(6) (1997)

    Google Scholar 

  11. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, New York (1982)

    MATH  Google Scholar 

  12. Zwiggelaara, R., Parra, T.C., Schumma, J.E., et al.: Model-based detection of spiculated lesions in mammograms. Medical Image Analysis 3(1), 39–62 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mencattini, A., Rabottino, G., Salmeri, M., Lojacono, R., Colini, E. (2008). Breast Mass Segmentation in Mammographic Images by an Effective Region Growing Algorithm. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_86

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88458-3_86

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88457-6

  • Online ISBN: 978-3-540-88458-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics