Contour-Based Image Registration Using Mutual Information

  • Nancy A. Álvarez
  • José M. Sanchiz
  • Jorge Badenas
  • Filiberto Pla
  • Gustavo Casañ
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)


Image registration is a problem that arises in many image processing applications whenever information from two or more scenes have to be aligned. In image registration the use of an adequate measure of alignment is a crucial issue. Current techniques are classified in two broad categories: area based and feature based. All methods include some similarity measure. In this paper a new measure that combines mutual information ideas, spatial information and feature characteristics, is proposed. Edge points are used as features, obtained from a Canny edge detector. Feature characteristics like location, edge strength and orientation are taken into account to compute a joint probability distribution of corresponding edge points in two images. Mutual information based on this function is minimized to find the best alignment parameters. The approach has been tested with a collection of portal images taken in real cancer treatment sessions, obtaining encouraging results.


Mutual Information Image Registration Edge Point Joint Probability Distribution Canny Edge Detector 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Langmack, A.: Portal Imaging. Br. J. Radiol. 74, 789–804 (2001)Google Scholar
  2. 2.
    Gottesfeld, L.: A survey of image registration techniques. ACM Computing Surveys 24, 325–376 (1992)CrossRefGoogle Scholar
  3. 3.
    Plattard, D., Soret, M., Troccaz, J., Vassal, P., Giraud, J., Champleboux, G., Artignan, X., Bolla, M.: Patient Set-Up using Portal Images: 2D/2D Image Registration Using Mutual Information. Computer Aided Surgery, 246–262 (2000)Google Scholar
  4. 4.
    Hristov, D.H., Fallone, B.G.: A gray-level image alignment algorithm for registration of portal images and digitally reconstructed radiographs. Med. Phys. 23, 75–84 (1996)CrossRefGoogle Scholar
  5. 5.
    Van Elsen, P.A., Pol, E., Viergever, M.A.: Medical image matching – a review with classification. ACM Computing Surveys 24, 325–376 (1992)CrossRefGoogle Scholar
  6. 6.
    Lester, H., Arrige, S.R.: A survey of hierarchical non-linear medical image registration. Pattern Recognition 32, 129–149 (1999)CrossRefGoogle Scholar
  7. 7.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  8. 8.
    Chmielewski, L., Kukolowicz, P.F., Gut, P., Dabrowski, A.: Assesment of the quality of radiotherapy with the use of portal and simulation images – the method and the software. Journal of Medical Informatics & Technologies 3, 171–179 (2002)Google Scholar
  9. 9.
    Leszczynski, K., Loose, S., Dunscombe, P.: Segmented chamfer matching for the registration of field borders in radiotherapy images. Phys Med. Biol. 40, 83–94 (1995)CrossRefGoogle Scholar
  10. 10.
    Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Trans PAMI 10, 849–865 (1988)Google Scholar
  11. 11.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16, 187–198 (1997)CrossRefGoogle Scholar
  12. 12.
    Viola, P., Wells, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24, 137–154 (1997)CrossRefGoogle Scholar
  13. 13.
    Rangarajan, A., Chui, H., Duncan, J.: Rigid point feature registration using mutual information. Medical Image Analysis 4, 1–17 (1999)Google Scholar
  14. 14.
    Pluim, J., Maintz, J.B., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Transactions on Medical Imaging 19, 809–814 (2000)CrossRefGoogle Scholar
  15. 15.
    West, J., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J. Comput. Assited Tomography 21, 554–566 (1997)CrossRefGoogle Scholar
  16. 16.
    Canny, J.F.: A Computational Approach to Edge Detection. IEEE TPAMI 8, 679–698 (1986)Google Scholar
  17. 17.
    Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Medical Image Analysis 3, 373–386 (1999)CrossRefGoogle Scholar
  18. 18.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nancy A. Álvarez
    • 1
  • José M. Sanchiz
    • 2
  • Jorge Badenas
    • 2
  • Filiberto Pla
    • 2
  • Gustavo Casañ
    • 2
  1. 1.Universidad de OrienteSantiagoCuba
  2. 2.Universidad Jaume ICastellónSpain

Personalised recommendations