Image Registration Using Markov Random Coefficient Fields

  • Edgar Román Arce-Santana
  • Alfonso Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4958)


Image Registration is central to different applications such as medical analysis, biomedical systems, image guidance, etc. In this paper we propose a new algorithm for multi-modal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on linear intensity transformation functions. The coefficients of these transformations are represented as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both the parameters that control the affine transformation of one of the images and the coefficient fields of the intensity transformations for each pixel.


Image Registration Markov Random Fields Bayesian Estimation Intensity Transformation Function 


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  1. 1.
    Banerjee, P.K., Toga, A.W.: Image aligment by integrated rotational and translation transformation matrix. Physics in Medical and Biology 39, 1969–1988 (1994)CrossRefGoogle Scholar
  2. 2.
    Besang, J.: Spatial interaction and statistical analysis of lattice sytems. J. Royal Staistical Soc. B 361(2), 192–236 (1974)Google Scholar
  3. 3.
    Cocosco, C.A., Kollokian, V., Kwan, R.K., Evans, A.C.: Brain web: Online interface to a 3DMRI simulated brain database. NeuroImage 5(2), Part 2/4, S425 (1997) (Proceedings of the 3rd International Conference on Functional Mapping of the Human Brain, Copenhagen, May 1997)Google Scholar
  4. 4.
    Ding, E., Kularatna, T., Satter, M.: Volumetric image registration by template matching. In: Medical Imaging 2000, pp. 1235–1246. SPIE, Bellinham, WA (2000)Google Scholar
  5. 5.
    Du, J., Tang, S., Jiang, T., Lu, Z.: Intensity based robust similarity for multimodal image registration. International Journal of Computer Mathematics 83, 49–57 (2006)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Fitzpatrick, J., West, J., Maurer, C.: Predicting error in rigid-body, point-based registration. IEEE Tranasctions on Medical Imaging 17, 694–702 (1998)CrossRefGoogle Scholar
  7. 7.
    Frantz, S., Rohr, K., Stiehl, H.S., Kim, S.I., Weese, J.: Validation point-based MR/CT registration based on semi-automatic landmark extraction. In: Proceeding of CARS 1999, pp. 233–237. Elsevier, Amsterdam (1999)Google Scholar
  8. 8.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)MATHCrossRefGoogle Scholar
  9. 9.
    Gonzales, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Prentice-Hall, NJ (2004)Google Scholar
  10. 10.
    Guimond, A., Roche, A., Ayache, N., Meunier, J.: Three-Dimensional Multimodal Brain Wraping Using the Demons Algorithm and Adaptive Intensity Corrections. IEEE Transaction on Medical Imaging 20(1), 58–69 (2001)CrossRefGoogle Scholar
  11. 11.
    Hsu, L., Loew, M.H., Ostuni, L.J.: Automated registration of CT and MR brain images using hierarchical shape representation. IEEE Engineering in Medicine and Biology Magazine 18, 40–47 (1999)CrossRefGoogle Scholar
  12. 12.
    Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Berlin (1995)Google Scholar
  13. 13.
    Marroquin, J.L., Mitter, S., Poggio, T.: Probabilistic Solution of Ill-Posed problems in Computational Vision. J. Am. Statistical Assoc. 82(397), 76–89 (1987)MATHCrossRefGoogle Scholar
  14. 14.
    Marroquin, J.L.: Detrministic Interactive Particle Models for Image Processing and Computer Graphics. Graphical Models and Image Processing 55(5), 408–417 (1996)CrossRefGoogle Scholar
  15. 15.
    Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, Heidelberg (1999)MATHGoogle Scholar
  16. 16.
    Shekhar, R., Zagrodsky, V.: Mutual Information-based rigid and non-rigid registration of ultrasound volumes. IEEE Transaction on Medical Imaging 21, 9–22 (2002)CrossRefGoogle Scholar
  17. 17.
    Thirion, J.-P.: Image matching as a diffusion process: An analogy with Maxwell’s demons. Med. Image Anal. 2, 243–260 (1998)CrossRefGoogle Scholar
  18. 18.
    Viola, P.A., Wells III, W.M., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal Volumen Registration by Maximization of Mutual Infromation. Medical Image Analysis 1, 5–51 (1996)Google Scholar
  19. 19.
    Woods, R.P., Mazziotta, J.C., Cherry, S.R.: MRI-PET registration with automated algorithm. Journal of Computer Asisted Tomography 17, 536–546 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Edgar Román Arce-Santana
    • 1
  • Alfonso Alba
    • 1
  1. 1.Facultad de CienciasUniversidad Autonoma de San Luis PotosiSan Luis PotosiMexico

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