Skip to main content
SpringerLink
Log in

A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Intensity inhomogeneity (IIH) or bias field in magnetic resonance imaging (MRI) severely affects quantitative image analysis. This paper presents a nonparametric IIH-correction strategy in MRI brain images by fusing multiple Gaussian surfaces. The IIH is modeled as a slowly varying multiplicative noise along with the actual tissue signals. The method does not require a priori knowledge on the intensity probability distribution; rather, it works directly on spatial domains using local image gradients. The method has four steps. Firstly, it extracts different potential tissue regions by considering image histogram. Secondly, an approximated bias field is estimated by fitting a Gaussian surface on the gradient map of each of the homogeneous tissue regions by considering its center as the center of mass. The intensity inhomogeneity field of the entire image is then obtained by fusion of these bias fields. Finally, this IIH field is iteratively removed from the image to obtain the IIH-corrected image. The proposed method is evaluated extensively on popular BrainWeb simulated MRI brain databases and also on some real-patient MRI brain images. Both qualitative and quantitative evaluations of the proposed method reveal its efficiency in removing the bias field in MRI brain images. The standard deviation, coefficient of variation of different tissue regions and coefficient of joint variation between gray matter and white matter are significantly reduced in greater proportion as compared to other standard methods in the case of T2-weighted MRI and come very closer to the ground truths.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Duncan, J.S., Ayache, N.: Medical image analysis: progress over two decades and the challenges ahead. IEEE Trans. Pattern Anal. Mach. Intell. 1, 85–106 (2000)

    Article  Google Scholar 

  2. Balafar, M.A., Ramli, A.R., Saripan, M.I., Mashohor, S.: Review of brain MRI image segmentation methods. J. Artif. Intell. 33, 261–274 (2010)

    Google Scholar 

  3. Pham, D.L., Xu, C., Prince, J.L.: A survey of current methods in medical image segmentation. Technical report JHU/ECE-99-01. Annu. Rev. Biomed. Eng. (1998)

  4. Olabarriaga, Smeulders, : Interaction in the segmentation of medical images: a survey’. Med. Image Anal. 5, 127–142 (2001)

    Article  Google Scholar 

  5. Li, C., Huang, R., Ding, Z., Gatenby, J.C., Metaxas, D.N.: A level set method for image segmentation in the presence of Intensity inhomogeneities with application to MRI’. IEEE Trans. Med. Imaging 20(7), 2007–2016 (2011)

    MathSciNet  Google Scholar 

  6. Tian, G., Xia, Y.: Hybrid genetic and variational expectation-maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation. IEEE Trans. Med. Imaging 15(3), 373–380 (2011)

    MathSciNet  Google Scholar 

  7. Vovk, U., Pernuš, F., Likar, B.: A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 26(3), 405–421 (2007)

    Article  Google Scholar 

  8. Hou, Z.: A review on MR image intensity inhomogeneity correction. Int. J. Biomed. Imaging 2006, 1–11 (2006)

    Article  Google Scholar 

  9. Madabhushi, A., Udupa, J.K.: Interplay between intensity standardization and Inhomogeneity correction in MR image processing. IEEE Trans. Med. Imaging 24(5), 561–576 (2005)

    Article  Google Scholar 

  10. Belaroussi, B., Milles, J., Carme, S., Zhu, Y.M., Benoit-Cattin, H.: Intensity non-uniformity correction in MRI: existing methods and their validation. Med. Image Anal. 10, 234–246 (2006)

    Article  Google Scholar 

  11. Brinkmann, B.H., Manduca, A., Robb, R.A.: Optimized homomorphic unsharp masking for MR gray scale inhomogeneity correction. IEEE Trans. Med. Imaging 17(2), 161–171 (April 1998)

  12. Tomazevic, D., Likar, B., Pernus, F.: Comparative evaluation of retrospective shading correction methods. J. Microsc. 208(3), 212–223 (2002)

    Article  MathSciNet  Google Scholar 

  13. Cohen, M.S., Dubois, R.M., Zeineh, M.M.: Rapid and effective correction of RF inhomogeneity for high field magnetic resonance imaging. Hum. Brain Mapp. 10, 204–211 (2000)

    Article  Google Scholar 

  14. Zhou, L.Q., Zhu, Y.M., Bergot, C., Laval-Jeantet, A.M., Bousson, V., Laredo, J.D., Laval-Jeantet, M.: A method of radio frequency inhomogeneity correction for brain tissue segmentation in MRI. Comput. Med. Imaging Graph. 25, 379–389 (2001)

    Article  Google Scholar 

  15. Han, C., Hatsukami, T.S., Yuan, C.: A multi-scale method for automatic correction of intensity non-uniformity in MR images. J. Magn. Reson. Imaging 13(3), 428–436 (2001)

    Article  Google Scholar 

  16. Lin, F., Chen, Y., Belliveau, J.W., Wald, L.L.: A wavelet-based approximation of surface coil sensitivity profiles for correction of image intensity inhomogeneity and parallel imaging reconstruction. Hum. Brain Mapp. 22(2), 133–144 (2004)

    Article  Google Scholar 

  17. Dawant, B.M., Zijdenbos, A.P., Margolin, R.A.: Correction of intensity variations in MR images for computer aided tissue classification. IEEE Trans. Med. Imaging 12(4), 770–781 (1993)

    Article  Google Scholar 

  18. Zhuge, Y., Odupa, J.K., Liu, J., Saha, P.K., Iwanage, T.: Scale-based method for correcting background intensity variation in acquired images. Proc. SPIE Med. Imaging 4686, 1103–1111 (2002)

    Article  Google Scholar 

  19. Meyer, C.R., Bland, P.H., Pipe, J.: Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans. Med. Imaging 14(1), 36–41 (1995)

    Article  Google Scholar 

  20. Lai, S.H., Fang, M.: A new variational shape-form-orientation approach for correcting intensity inhomogeneities in magnetic resonance images. Med. Image Anal. 3, 409–424 (1999)

    Article  Google Scholar 

  21. Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. Kim, S.G., Ng, S. K., Mclachlan, G.J., Deming, W.: Segmentation of brain MR images with bias field correction. In: Proceedings of the APRS Workshop Digital Image Computing, WDIC 2003, Australia (2003)

  24. 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. Imaging 20(1), 45–57 (2001)

    Article  Google Scholar 

  25. Liew, A.W., Yan, H.: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation. IEEE Trans. Med. Imaging 22(9), 1063–1075 (2003)

    Article  Google Scholar 

  26. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data”. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)

  27. Vovk, U., Pernus, F., Likar, B.: MRI intensity inhomogeneity correction by combining intensity and spatial information. Phys. Med. Biol. 49, 4119–4133 (2004)

  28. Likar, B., Viergerver, A., Pernŭ, Franjo: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imaging 20(12), 1398–1410 (2001)

    Article  Google Scholar 

  29. Cocosco, C.A., Kollokian, V., Kwan, K.R.K.S., Evans, A.C.: Brainweb: online interface to a 3D MRI simulated brain database. Neuroimage 5, 425–425 (1997)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the All India Council for Technical Education (AICTE) sponsored research project (F.No.: 8023/BOR/RID/RPS-130/2008-09, dated: 12-03-09). The authors are very grateful to the Advanced Medical Research Institute (AMRI) Hospital at Dhakuria, Kolkata for providing real-patient MRI brain images. We are also thankful to the anonymous reviewers for their comments, which have been very useful to improve this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Neotia Institute of Technology, Management and Science, Diamond Harbour Road, Sarisa, South 24 Parganas, India

    Sudip Kumar Adhikari

  2. Department of Computer Science and Engineering, Jadavpur University, 188, Raja S. C. Mullick Road, Kolkata, 700 032, India

    Jamuna Kanta Sing, Dipak Kumar Basu & Mita Nasipuri

  3. Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA

    Punam Kumar Saha

Authors
  1. Sudip Kumar Adhikari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamuna Kanta Sing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dipak Kumar Basu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mita Nasipuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Punam Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamuna Kanta Sing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adhikari, S.K., Sing, J.K., Basu, D.K. et al. A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces. SIViP 9, 1945–1954 (2015). https://doi.org/10.1007/s11760-014-0689-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0689-5

Keywords

Search

Navigation