Image Registration Using Markov Random Coefficient Fields
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.
KeywordsImage Registration Markov Random Fields Bayesian Estimation Intensity Transformation Function
Unable to display preview. Download preview PDF.
- 2.Besang, J.: Spatial interaction and statistical analysis of lattice sytems. J. Royal Staistical Soc. B 361(2), 192–236 (1974)Google Scholar
- 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.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
- 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
- 9.Gonzales, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Prentice-Hall, NJ (2004)Google Scholar
- 12.Li, S.Z.: Markov Random Field Modeling in Computer Vision. Springer, Berlin (1995)Google Scholar
- 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