Skip to main content
SpringerLink
Log in

Multispectral MRI image segmentation using Markov random field model

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

Abstract

Magnetic resonance imaging (MRI) is used to capture images in different modalities such as T1-weighted, T2-weighted, and PD-weighted. This paper proposes a new method for the fusion of different channels in MRI image segmentation. In the reported work, a new feature vector for multispectral MRI brain segmentation is proposed. Fuzzy C-means clustering method is applied on the three different extracted feature vectors, and results are reported. Experimental results show that the proposed feature vector presents good noise immunity. Paper reports a new segmentation method based on Markov random field and the proposed feature vector to combine spatial and spectral information for MRI image segmentation. The proposed method was applied on the BrainWeb MRI image dataset with added noise, and the segmentation results are reported and compared with some known reported works.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Lin, G.C., Wang, W.J., Kang, C.C., Wang, C.M.: Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing. Magn. Reson. Imaging 30(2), 230–246 (2012)

    Article  Google Scholar 

  2. Yousefi, S., Azmi, R., Zahedi, M.: Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms. Med. Image Anal. 16(4), 840–848 (2012)

    Article  Google Scholar 

  3. Ghasemi, J., Ghaderi, R., Karami Mollaei, M., Hojjatoleslami, S.: A novel fuzzy Dempster–Shafer inference system for brain MRI segmentation. Inf. Sci. 223, 205–220 (2013)

    Article  Google Scholar 

  4. Szilágyi, L., Szilágyi, S.M., Benyó, B.: Efficient inhomogeneity compensation using fuzzy C-means clustering models. Comput. Methods Prog. Biomed. 108(1), 80–89 (2012)

    Article  Google Scholar 

  5. Greenspan, H., Ruf, A., Goldberger, J.: Constrained Gaussian mixture model framework for automatic segmentation of MR brain images. IEEE Trans. Med. Imaging 25(9), 1233–1245 (2006)

    Article  Google Scholar 

  6. Tohka, J., Krestyannikov, E., Dinov, I., Shattuck, D., Ruotsalainen, U., Toga, A.: Genetic algorithms for finite mixture model based tissue classification in brain MRI. In: Proceedings of the European Medical and Biological Engineering Conference (IFMBE), pp. 4077–4082 (2005)

  7. Tohka, J., Krestyannikov, E., Dinov, I.D., Graham, A., Shattuck, D.W., Ruotsalainen, U., Toga, A.W.: Genetic algorithms for finite mixture model based voxel classification in neuroimaging. IEEE Trans. Med. Imaging 26(5), 696–711 (2007)

    Article  Google Scholar 

  8. Dey, V., Zhang, Y., Zhong, M.: A review on image segmentation techniques with remote sensing perspective. In: Proceedings of the International Society for Photogrammetry and Remote Sensing Symposium (ISPRS10), Vienna, pp. 5–7 (2010)

  9. Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imaging 18(10), 897–908 (1999)

    Article  Google Scholar 

  10. Balafar, M.: Gaussian mixture model based segmentation methods for brain MRI images. Artif. Intell. Rev. 41(3), 1–11 (2012)

    Google Scholar 

  11. Dubes, R., Jain, A., Nadabar, S., Chen, C.: MRF model-based algorithms for image segmentation. In: Proceedings of the 10th International Conference Pattern Recognition, pp. 808–814 (1990)

  12. Rajapakse, J.C., Giedd, J.N., Rapoport, J.L.: Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imaging 16(2), 176–186 (1997)

    Article  Google Scholar 

  13. Marroquín, J.L., Vemuri, B.C., Botello, S., Calderon, E., Fernandez-Bouzas, A.: An accurate and efficient Bayesian method for automatic segmentation of brain MRI. IEEE Trans. Med. Imaging 21(8), 934–945 (2002)

    Article  Google Scholar 

  14. 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 

  15. Pham, D., Prince, J.L., Xu, C., Dagher, A.P.: An automated technique for statistical characterization of brain tissues in magnetic resonance imaging. Int. J. Pattern Recognit. Artif. Intell. 11(08), 1189–1211 (1997)

    Article  Google Scholar 

  16. Caldairou, B., Passat, N., Habas, P.A., Studholme, C., Rousseau, F.: A non-local fuzzy segmentation method: application to brain MRI. Pattern Recognit. 44(9), 1916–1927 (2011)

    Article  Google Scholar 

  17. Rivest-Hénault, D., Cheriet, M.: Unsupervised MRI segmentation of brain tissues using a local linear model and level set. Magn. Reson. Imaging 29(2), 243–259 (2011)

    Article  Google Scholar 

  18. Wu, T., Bae, M.H., Zhang, M., Pan, R., Badea, A.: A prior feature SVM-MRF based method for mouse brain segmentation. NeuroImage 59(3), 2298–2306 (2012)

    Article  Google Scholar 

  19. Riklin-Raviv, T., Van Leemput, K., Menze, B.H., Wells III, W.M., Golland, P.: Segmentation of image ensembles via latent atlases. Med. Image Anal. 14(5), 654–665 (2010)

    Article  Google Scholar 

  20. Wang, Z., Ziou, D., Armenakis, C., Li, D., Li, Q.: A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens. 43(6), 1391–1402 (2005)

    Article  Google Scholar 

  21. Nunez, J., Otazu, X., Fors, O., Prades, A., Pala, V., Arbiol, R.: Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans. Geosci. Remote Sens. 37(3), 1204–1211 (1999)

    Article  Google Scholar 

  22. Besag, J.: Statistical analysis of non-lattice data. Statistician 24(3), 179–195 (1975)

    Article  MathSciNet  Google Scholar 

  23. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 721–741 (1984)

    Article  MATH  Google Scholar 

  24. Collins, D.L., Zijdenbos, A.P., Kollokian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17(3), 463–468 (1998)

    Article  Google Scholar 

  25. Ferreira da Silva, A.R.: A Dirichlet process mixture model for brain MRI tissue classification. Med. Image Anal. 11(2), 169–182 (2007)

    Article  Google Scholar 

  26. The homepage for the LONI (“Laboratory of Neuro Imaging”) software package, http://www.loni.usc.edu/Software/, as visited on 2014

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, University Road, Hengam Street, Resalat Square, Narmak, 16846-13114, Tehran, Iran

    Ali Ahmadvand & Peyman Kabiri

Authors
  1. Ali Ahmadvand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peyman Kabiri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peyman Kabiri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmadvand, A., Kabiri, P. Multispectral MRI image segmentation using Markov random field model. SIViP 10, 251–258 (2016). https://doi.org/10.1007/s11760-014-0734-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0734-4

Keywords

Search

Navigation