Multi-modal Image Registration Using the Generalized Survival Exponential Entropy

  • Shu Liao
  • Albert C. S. Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


This paper introduces a new similarity measure for multi-modal image registration task. The measure is based on the generalized survival exponential entropy (GSEE) and mutual information (GSEE-MI). Since GSEE is estimated from the cumulative distribution function instead of the density function, it is observed that the interpolation artifact is reduced. The method has been tested on four real MR-CT data sets. The experimental results show that the GSEE-MI-based method is more robust than the conventional MI-based method. The accuracy is comparable for both methods.


Mutual Information Image Registration Registration Error Image Registration Method Translational Probe 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Wells, W., Viola, P., et al.: Multi-Modal Volume Registration by Maximization of Mutual Information. MedIA 1(1), 35–51 (1996)Google Scholar
  2. 2.
    Maes, F., Collignon, A., et al.: Multimodality Image Registration by Maximization of Mutual Information. TMI 16(2), 187–198 (1997)Google Scholar
  3. 3.
    Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. TMI 22(8), 986–1004 (2003)Google Scholar
  4. 4.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley and Sons, Chichester (1991)MATHCrossRefGoogle Scholar
  5. 5.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford U. Press (1995)Google Scholar
  6. 6.
    Maes, F., Collignon, A., et al.: Multimodality Image Registration by Maximization of Mutual Information. TMI 16(2), 187–198 (1997)Google Scholar
  7. 7.
    Zografos, K., Nadarajah, S.: Survival Exponential Entropies. IEEE Trans. on Information Theory 51(3), 1239–1246 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–432 (1948)MATHMathSciNetGoogle Scholar
  9. 9.
    Wang, F., Vemuri, B., Rao, M., Chen, Y.: A New & Robust Information Theoretic Measure and its Application to Image Alignment. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 388–400. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Press, W., Teukolsky, S., et al.: Numerical Recipes in C. Cambridge University Press, Cambridge (1992)MATHGoogle Scholar
  11. 11.
    Hajnal, J.V., Hill, D.L.G., Hawkes, D.J.: Medical Image Registration. CRC Press LLC (2001)Google Scholar
  12. 12.
    Zhu, Y., Cochoff, S.: Likelihood Maximization Approach to Image Registration. TIP 11, 1417–1426 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shu Liao
    • 1
  • Albert C. S. Chung
    • 1
  1. 1.Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyHong Kong

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