Intensity Gradient Based Registration and Fusion of Multi-modal Images

  • Eldad Haber
  • Jan Modersitzki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convex and has typically many local maxima.

This observation motivate us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multi-modal images. In this work we investigate an alternative distance measure which is based on normalized gradients and compare its performance to Mutual Information. We call the new distance measure Normalized Gradient Fields (NGF).


Mutual Information Image Registration Intensity Gradient Joint Inversion Registration Problem 
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.
    Ascher, U., Haber, E., Haung, H.: On effective methods for implicit piecewise smooth surface recovery. SISC 28(1), 339–358 (2006)MATHGoogle Scholar
  2. 2.
    Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Cocosco, C.A., Kollokian, V., Kwan, R.K.-S., Evans, A.C.: BrainWeb MR simulator, available at
  4. 4.
    Collignon, A., Vandermeulen, A., Suetens, P., Marchal, G.: Multi-modality medical image registration based on information theory. Kluwer Academic Publishers: Computational Imaging and Vision 3, 263–274 (1995)Google Scholar
  5. 5.
    Dennis, J.E., Schnabel, R.B.: Numerical methods for unconstrained optimization and nonlinear equations. SIAM, Philadelphia (1996)MATHGoogle Scholar
  6. 6.
    Droske, M., Rumpf, M.: A variational approach to non-rigid morphological registration. SIAM Appl. Math. 64(2), 668–687 (2004)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Gallardo, L.A., Meju, M.A.: Characterization of heterogeneous near-surface materials by joint 2d inversion of dc resistivity and seismic data. Geophys. Res. Lett. 30(13), 1658–1664 (2003)CrossRefGoogle Scholar
  8. 8.
    Golub, G., Heath, M., Wahba, G.: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21, 215–223 (1979)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Haber, E., Oldenburg, D.: Joint inversion a structural approach. Inverse Problems 13, 63–67 (1997)MATHCrossRefGoogle Scholar
  10. 10.
    Modersitzki, J.: Numerical methods for image registration, Oxford (2004)Google Scholar
  11. 11.
    Park, H., Bland, P.H., Brock, K.K., Meyer, C.R.: Adaptive registration using local information measures. Medical Image Analysis 8, 465–473 (2004)CrossRefGoogle Scholar
  12. 12.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE TMI 19(8), 809–814 (2000)Google Scholar
  13. 13.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Interpolation artefacts in mutual information based image registration. In: Hanson, K.M. (ed.) Proceedings of the SPIE 2004, Medical Imaging, 1999, vol. 3661, pp. 56–65. SPIE (2004)Google Scholar
  14. 14.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22, 986–1004 (1999)CrossRefGoogle Scholar
  15. 15.
    Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. In: Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics: computational issues in nonlinear science, pp. 259–268 (1992)Google Scholar
  16. 16.
    Silverman, R.: Density estimation for statistics and data analysis. Chapman and Hall, Boca Raton (1992)Google Scholar
  17. 17.
    Unser, M., Thévenaz, P.: Stochastic sampling for computing the mutual information of two images. In: Proceedings of the Fifth International Workshop on Sampling Theory and Applications (SampTA 2003), Strobl, Austria, May 26-30, 2003, pp. 102–109 (2003)Google Scholar
  18. 18.
    Viola, P.A.: Alignment by maximization of mutual information, Ph.D. thesis, Massachusetts Institute of Technology (1995)Google Scholar
  19. 19.
    Wahba, G.: Spline models for observational data. SIAM, Philadelphia (1990)MATHGoogle Scholar
  20. 20.
    Yoo, T.S.: Insight into images: Principles and practice for segmentation, registration, and image analysis. AK Peters Ltd (2004)Google Scholar
  21. 21.
    Zhang, J., Morgan, F.D.: Joint seismic and electrical tomography. In: Proceedings of the EEGS Symposium on Applications of Geophysics to Engineering and Environmental Problems, pp. 391–396 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Eldad Haber
    • 1
  • Jan Modersitzki
    • 2
  1. 1.Mathematics and Computer ScienceEmory UniversityAtlantaUSA
  2. 2.Institute of MathematicsLübeckGermany

Personalised recommendations