Advertisement

A Joint Bayesian Framework for MR Brain Scan Tissue and Structure Segmentation Based on Distributed Markovian Agents

  • Benoit Scherrer
  • Florence Forbes
  • Catherine Garbay
  • Michel Dojat
Part of the Studies in Computational Intelligence book series (SCI, volume 309)

Abstract

In most approaches, tissue and subcortical structure segmentations of MR brain scans are handled globally over the entire brain volume through two relatively independent sequential steps. We propose a fully Bayesian joint model that integrates within a multi-agent framework local tissue and structure segmentations and local intensity distribution modeling. It is based on the specification of three conditional Markov Random Field (MRF) models. The first two encode cooperations between tissue and structure segmentations and integrate a priori anatomical knowledge. The third model specifies a Markovian spatial prior over the model parameters that enables local estimations while ensuring their consistency, handling this way nonuniformity of intensity without any bias field modeling. The complete joint model provides then a sound theoretical framework for carrying out tissue and structure segmentations by distributing a set of local agents that estimate cooperatively local MRF models. The evaluation, using a previously affine-registered atlas of 17 structures, was performed using both phantoms and real 3T brain scans. It shows good results and in particular robustness to nonuniformity and noise with a low computational cost. The innovative coupling of agent-based and Markov-centered designs appears as a robust, fast and promising approach to MR brain scan segmentation.

Keywords

Medical Imaging Multi-Agents Medical Image Processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5), 856–876 (2001)CrossRefGoogle Scholar
  2. 2.
    Rajapakse, J.C., Giedd, J.N., Rapoport, J.L.: Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imag. 16(2), 176–186 (1997)CrossRefGoogle Scholar
  3. 3.
    Scherrer, B., Dojat, M., Forbes, F., Garbay, C.: LOCUS: LOcal Cooperative Unified Segmentation of MRI brain scans. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 1066–1074. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Distributed Local MRF Models for Tissue and Structure Brain Segmentation. IEEE Trans. Med. Imag. 28, 1296–1307 (2009)CrossRefGoogle Scholar
  5. 5.
    Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: Fully Bayesian Joint Model for MR Brain Scan Tissue and Structure Segmentation. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1066–1074. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Byrne, W., Gunawardana, A.: Convergence theorems of Generalized Alternating Minimization Procedures. J. Machine Learning Research 6, 2049–2073 (2005)MathSciNetGoogle Scholar
  7. 7.
    Shariatpanahi, H.F., Batmanghelich, N., Kermani, A.R.M., Ahmadabadi, M.N., Soltanian-Zadeh, H.: Distributed behavior-based multi-agent system for automatic segmentation of brain MR images. In: International Joint Conference on Neural Networks, IJCNN 2006 (2006)Google Scholar
  8. 8.
    Richard, N., Dojat, M., Garbay, C.: Distributed Markovian segmentation: Application to MR brain scans. Pattern Recognition 40(12), 3467–3480 (2007)MATHCrossRefGoogle Scholar
  9. 9.
    Germond, L., Dojat, M., Taylor, C., Garbay, C.: A cooperative framework for segmentation of MRI brain scans. Artificial Intelligence in Medicine 20, 77–94 (2000)CrossRefGoogle Scholar
  10. 10.
    Scherrer, B., Dojat, M., Forbes, F., Garbay, C.: Agentification of Markov model based segmentation: Application to magnetic resonance brain scans. Artificial Intelligence in Medicine 46, 81–95 (2009)CrossRefGoogle Scholar
  11. 11.
    McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. Wiley, Chichester (1996)Google Scholar
  12. 12.
    Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis, 2nd edn. Chapman and Hall, Boca Raton (2004)MATHGoogle Scholar
  13. 13.
    Lafferty, J., McCallum, A., Peirera, F.: Conditional Random Fields: Probabilistic models for segmenting and labelling sequence data. In: 18th Inter. Conf. on Machine Learning (2001)Google Scholar
  14. 14.
    Minka, T.: Discriminative models not discriminative training. Tech. Report MSR-TR-2005-144, Microsoft Research (2005)Google Scholar
  15. 15.
    Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. In: Jordan, M.I. (ed.) Learning in Graphical Models, pp. 105–162. The MIT Press, Cambridge (1999)Google Scholar
  16. 16.
    Celeux, G., Forbes, F., Peyrard, N.: EM procedures using mean field-like approximations for Markov model-based image segmentation. Pat. Rec. 36, 131–144 (2003)MATHCrossRefGoogle Scholar
  17. 17.
    Besag, J.: On the statistical analysis of dirty pictures. J. Roy. Statist. Soc. Ser. B 48(3), 259–302 (1986)MATHMathSciNetGoogle Scholar
  18. 18.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. Roy. Statist. Soc. Ser. B 36(2), 192–236 (1974)MATHMathSciNetGoogle Scholar
  19. 19.
    Ashburner, J., Friston, K.J.: Unified Segmentation. NeuroImage 26, 839–851 (2005)CrossRefGoogle Scholar
  20. 20.
    Shattuck, D.W., Sandor-Leahy, S.R., Schaper, K.A., Rottenberg, D.A., Leahy, R.M.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13, 856–876 (2001)CrossRefGoogle Scholar
  21. 21.
    Van Leemput, K., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based bias field correction in MR images of the brain. IEEE Trans. Med. Imag. 18, 885–896 (1999)CrossRefGoogle Scholar
  22. 22.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)CrossRefGoogle Scholar
  23. 23.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the Expectation-Maximisation algorithm. IEEE Trans. Med. Imag. 20, 45–47 (2001)CrossRefGoogle Scholar
  24. 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. Imag. 17, 463–468 (1998)CrossRefGoogle Scholar
  25. 25.
    Jenkinson, M., Smith, S.M.: A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5, 143–156 (2001)CrossRefGoogle Scholar
  26. 26.
    Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Trans. Med. Imag. 23, 903–921 (2004)CrossRefGoogle Scholar
  27. 27.
    Ciofolo, C., Barillot, C.: Atlas-based segmentation of 3d cerebral structures with competitive level sets and fuzzy control. Medical Image Analysis 13, 456–470 (2009)CrossRefGoogle Scholar
  28. 28.
    Pohl, K.M., Fisher, J., Grimson, E., Kikinis, R., Wells, W.: A Bayesian model for joint segmentation and registration. NeuroImage 31, 228–239 (2006)CrossRefGoogle Scholar
  29. 29.
    Scherrer, B., Forbes, F., Garbay, C., Dojat, M.: A Conditional Random Field approach for coupling local registration with robust tissue and structure segmentation. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5762, pp. 540–548. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., Kadie, C.: Dependency networks for inference, collaborative filtering and data visualization. J. Machine Learning Research 1, 49–75 (2000)CrossRefGoogle Scholar
  31. 31.
    Arnold, B.C., Castillo, E., Sarabia, J.M.: Conditionally specified distributions: an introduction. Statistical Science 16(3), 249–274 (2001)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Benoit Scherrer
    • 1
    • 3
    • 4
  • Florence Forbes
    • 2
  • Catherine Garbay
    • 3
  • Michel Dojat
    • 1
    • 4
  1. 1.INSERM U836La TroncheFrance
  2. 2.Laboratoire Jean Kuntzman, MISTIS TeamINRIAMontbonnotFrance
  3. 3.Laboratoire d’Informatique de GrenobleFrance
  4. 4.Institut des Neurosciences GrenobleUniversité Joseph FourierLa TroncheFrance

Personalised recommendations