Skip to main content
SpringerLink
Log in

Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation

  • Conference paper
Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5164))

Included in the following conference series:

Abstract

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This paper proposes a multiple stage fuzzy c-means (FCM) based algorithm for the estimation and compensation of INU, by modeling it as a slowly varying additive or multiplicative noise, supported by a pre-filtering technique for Gaussian and impulse noise elimination. The slowly varying behavior of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, based on a morphological criterion. The experiments using 2-D synthetic phantoms and real MR images show, that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and segmentation techniques.

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 139.00
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. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imag. 21, 193–199 (2002)

    Article  Google Scholar 

  2. Axel, L., Costanini, J., Listerud, J.: Inhomogeneity correction in surface-coil MR imaging. Amer. J. Roentgenol. 148, 418–420 (1987)

    Google Scholar 

  3. Bezdek, J.C., Pal, S.K.: Fuzzy models for pattern recognition. IEEE Press, Piscataway (1991)

    Google Scholar 

  4. Brinkmann, B.H., Manduca, A., Robb, R.A.: Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE Trans. Med. Imag. 17, 161–171 (1998)

    Article  Google Scholar 

  5. Cai, W., Chen, S., Zhang, D.Q.: Fast and robust fuzzy c-means algorithms incorporating local information for image segmentation. Patt. Recogn. 40, 825–838 (2007)

    Article  MATH  Google Scholar 

  6. Internet Brain Segmentation Repository, http://www.cma.mgh.harvard.edu/ibsr

  7. Johnston, B., Atkins, M.S., Mackiewich, B., Anderson, M.: Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans. Med. Imag. 15, 154–169 (1996)

    Article  Google Scholar 

  8. Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction of MR images of the brain. IEEE Trans. Med. Imag. 18, 885–896 (1999)

    Article  Google Scholar 

  9. Li, X., Li, L.H., Lu, H.B., Liang, Z.G.: Partial volume segmentation of brain magnetic resonace images based on maximum a posteriori probability. Med. Phys. 32, 2337–2345 (2005)

    Article  Google Scholar 

  10. Liew, A.W.C., Hong, Y.: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imag. 22, 1063–1075 (2003)

    Article  Google Scholar 

  11. Nagy, L., Benyó, B.: Filtering and contrast enhancement on subtracted direct digital angiograms. In: Ann. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 26, pp. 1533–1536. San Francisco (2004)

    Google Scholar 

  12. Pham, D.L., Prince, J.L.: An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneity. Patt. Recogn. Lett. 20, 57–68 (1999)

    Article  MATH  Google Scholar 

  13. Pham, D.L., Prince, J.L.: Adaptive fuzzy segmentation of magnetic resonance images. IEEE Trans. Med. Imag. 18, 737–752 (1999)

    Article  Google Scholar 

  14. Siyal, M.Y., Yu, L.: An intelligent modified fuzzy c-means based algorithm for bias field estimation and segmentation of brain MRI. Patt. Recogn. Lett. 26, 2052–2062 (2005)

    Article  Google Scholar 

  15. Szilágyi, L.: Medical image processing methods for the development of a virtual endoscope. Period. Polytech. Ser. Electr. Eng. 50(1–2), 69–78 (2006)

    Google Scholar 

  16. Szilágyi, L., Szilágyi, S.M., Benyó, Z.: Efficient Feature Extraction for Fast Segmentation of MR Brain Images. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 611–620. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Vovk, U., Pernuš, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imag. 26, 405–421 (2007)

    Article  Google Scholar 

  18. Wells, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive segmentation of MRI data. IEEE Trans. Med. Imag. 15, 429–442 (1996)

    Article  Google Scholar 

  19. 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, 45–57 (2001)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary

    László Szilágyi & Zoltán Benyó

  2. Faculty of Technical and Human Science, Sapientia - Hungarian Science University of Transylvania, Târgu-Mureş, Romania

    László Szilágyi, Sándor M. Szilágyi & László Dávid

Authors
  1. László Szilágyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sándor M. Szilágyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. László Dávid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zoltán Benyó
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Véra Kůrková Roman Neruda Jan Koutník

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Szilágyi, L., Szilágyi, S.M., Dávid, L., Benyó, Z. (2008). Multi-stage FCM-Based Intensity Inhomogeneity Correction for MR Brain Image Segmentation. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87559-8_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87558-1

  • Online ISBN: 978-3-540-87559-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation