Image Registration from Mutual Information of Edge Correspondences

  • N. A. Alvarez
  • J. M. Sanchiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)

Abstract

Image registration is a fundamental task in image processing. Its aim is to match two or more pictures taken with the same or from different sensors, at different times or from different viewpoints. In image registration the use of an adequate measure of alignment is a crucial issue. Current techniques are classified in two broad categories: pixel 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 obtained from a Canny edge detector are used as features. 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 maximized to find the best alignment parameters. The approach has been tested with a collection of medical images (Nuclear Magnetic Resonance and radiotherapy portal images) and conventional video sequences, obtaining encouraging results.

Keywords

Mutual Information Video Sequence Joint Probability Image Registration Edge Point 
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.

References

  1. 1.
    Pluim, J.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 (2003)CrossRefGoogle Scholar
  2. 2.
    Viola, P., Wells, W.M.: Alignment by maximization of mutual information. International Journal on Computer Vision 24, 137–154 (1997)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Tomazevic, D., Likar, B., Pernus, F.: Multi-feature mutual information. In: Sonka, M. (ed.) Medical Imaging: Image Processing, vol. 5070, pp. 234–242. SPIE Press (2004)Google Scholar
  5. 5.
    Lester, H., Arriage, S.R.: A survey of hierarchical non-linear medical image registration. Pattern Recognition 32, 129–149 (1999)CrossRefGoogle Scholar
  6. 6.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  7. 7.
    Maintz, J., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1999)CrossRefGoogle Scholar
  8. 8.
    Leszczynski, K., Loose, S., Dunscombe, P.: Segmented chamfer matching for the registration of field borders in radiotherapy images. Physics Medicine and Bilogy 40, 83–94 (1995)CrossRefGoogle Scholar
  9. 9.
    Borgefors, G.: Hierarchical chamfer matching: a parametric edge matching algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 849–865 (1988)CrossRefGoogle Scholar
  10. 10.
    Hristov, D.H., Fallone, B.G.: A gray-level image alignment algorithm for registration of portal images and digitally reconstructed radiographs. Medical Physics 23, 75–84 (1996)CrossRefGoogle Scholar
  11. 11.
    Langmack, K.A.: Portal imaging. The British Journal of Radiology 74, 789–804 (2001)Google Scholar
  12. 12.
    Kim, J., Fessler, J.A., Lam, K.L., Balter, J.M., Ten-Haken, R.K.: A feasibility study of mutual information based setup error estimation for radiotherapy. Medical Physics 28, 2507–2517 (2001)CrossRefGoogle Scholar
  13. 13.
    Rangarajan, A., Chui, H., Duncan, J.: Rigid point feature registration using mutual information. Medical Image Analysis 3, 425–440 (1999)CrossRefGoogle 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. Journal of Computer Assisted Tomography 21, 554–566 (1997)CrossRefGoogle Scholar
  16. 16.
    Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physcis in Medicine and Biology 46, R1–R45 (2001)CrossRefGoogle Scholar
  17. 17.
    Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: Integrated intensity and point-feature nonrigid registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763–770. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  19. 19.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • N. A. Alvarez
    • 1
  • J. M. Sanchiz
    • 2
  1. 1.Universidad de OrienteSantiago de CubaCuba
  2. 2.Universidad Jaume ICastellónSpain

Personalised recommendations