Abstract
We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chung, A.C.S., Wells III, W.M.W., Norbash, A., Grimson, W.E.L.: Multi-modal image registration by minimizing kullback-leibler distance. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2489, pp. 525–532. Springer, Heidelberg (2002)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., New York (1991)
Kullback, S.: Information Theory and Statistics. John Wiley and Sons, New York (1959)
Leventon, M., Grimson, W.E.L.: Multi-modal volume registration using joint intensity distributions. In: First International Conference on Medical Image Computing and Computer-Assisted Intervention (1998)
Machado, A.M.C., Campos, M.F.M., Gee, J.C.: Bayesian model for intensity mapping in magnetic resonance image registration. Journal of Electronic Imaging 12(1), 31–39 (2003)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 567–578. Springer, Heidelberg (2000)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3d medical image alignment. Pattern Recognition 32(1), 71–86 (1999)
Wells III, W.M., Viola, P., Kikinis, R.: Multi-modal volume registration by maximization of mutual information [medical imaging]. In: Proceedings of 2nd International Symposium on Medical Robotics and Computer Assisted Surgery, pp. 55–62, 358 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zöllei, L., Fisher, J.W., Wells, W.M. (2003). A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration. In: Taylor, C., Noble, J.A. (eds) Information Processing in Medical Imaging. IPMI 2003. Lecture Notes in Computer Science, vol 2732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45087-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-45087-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40560-3
Online ISBN: 978-3-540-45087-0
eBook Packages: Springer Book Archive