Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images

  • S. Ourselin
  • A. Roche
  • S. Prima
  • N. Ayache
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1935)


In order to improve the robustness of rigid registration algorithms in various medical imaging problems, we propose in this article a general framework built on block matching strategies. This framework combines two stages in a multi-scale hierarchy. The first stage consists in finding for each block (or subregion) of the first image, the most similar subregion in the other image, using a similarity criterion which depends on the nature of the images. The second stage consists in finding the global rigid transformation which best explains most of these local correspondances. This is done with a robust procedure which allows up to 50% of false matches. We show that this approach, besides its simplicity, provides a robust and efficient way to rigidly register images in various situations. This includes for instance the alignment of 2D histological sections for the 3D reconstructions of trimmed organs and tissues, the automatic computation of the mid-sagittal plane in multimodal 3D images of the brain, and the multimodal registration of 3D CT and MR images of the brain. A quantitative evaluation of the results is provided for this last example, as well as a comparison with the classical approaches involving the minimization of a global measure of similarity based on Mutual Information or the Correlation Ratio. This shows a significant improvement of the robustness, for a comparable final accuracy. Although slightly more expensive in terms of computational requirements, the proposed approach can easily be implemented on a parallel architecture, which opens potentialities for real time applications using a large number of processors.


Mutual Information Block Match Rigid Registration Correlation Ratio 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]
    Brown, G.L.: A Survey of Image Registration Techniques. ACM Computing Surveys 24(4), 325–376 (1992)CrossRefGoogle Scholar
  2. [2]
    Collins, D.L., Evans, A.C.: ANIMAL: Validation and Applications of Nonlinear Registration-Based Segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11(8), 1271–1294 (1997)CrossRefGoogle Scholar
  3. [3]
    Eggert, D.W., Lorusso, A., Fisher, R.B.: Estimating 3D Rigid Body transformations: A Comparison of Four Major Algorithms. Machine Vision Applications, Special Issue on Performance Characteristics of Vision Algorithms 9(5/6), 272–290 (1997)Google Scholar
  4. [4]
    Gaens, T., Maes, F., Vandermeulen, D., Suetens, P.: Non-rigid Multimodal Image Registration Using Mutual Information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1099–1106. Springer, Heidelberg (1998)Google Scholar
  5. [5]
    Hibbard, L.S., Hawkings, R.A.: Objective image alignment for three-dimensional reconstruction of digital autoradiograms. Journal of Neuroscience Method 26, 55–74 (1988)CrossRefGoogle Scholar
  6. [6]
    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)CrossRefGoogle Scholar
  7. [7]
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  8. [8]
    Maintz, J.B.A., Meijering, E.H.W., Viergever, M.A.: General multimodal elastic registration based on mutual information. In: Hanson, K.M. (ed.) Medical Imaging: Image Processing, Bellingham, WA. Proc. SPIE, vol. 3338. SPIE Press, Bellingham (1998)Google Scholar
  9. [9]
    Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D Structure from Serial Histological Sections. In: Image and Vision Computing (2000) (in press)Google Scholar
  10. [10]
    Pluim, J.P.W., Maintz, J.B.A., Viergeve, M.A.: Mutual information matching and interpolation artefacts. In: Hanson, K.M. (ed.) Medical Imaging: Image Processing, Bellingham, WA. Proc. SPIE, vol. 3661. SPIE Press, Bellingham (1999)Google Scholar
  11. [11]
    Prima, S., Ourselin, S., Ayache, N.: Computation of the Mid-Sagittal Plane in 3D Images of the Brain. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842-3, pp. 685–701. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. [12]
    Roche, A., Malandain, G., Ayache, N., Prima, S.: Towards a Better Comprehension of Similarity Measures used in Medical Image Registration. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 555–566. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  13. [13]
    Roche, A., Malandain, G., Pennec, X., Ayache, N.: The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1115–1124. Springer, Heidelberg (1998)Google Scholar
  14. [14]
    Rousseeuw, P.J., Van Driessen, K.: Computing LTS Regression for Large Data Sets. Technical report, Statistics Group, University of Antwerp (1999)Google Scholar
  15. [15]
    Rousseuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection, 1st edn. Wiley Series in Probability and Mathematical Statistics (1987)Google Scholar
  16. [16]
    Viola, P.: Alignment by Maximisation of Mutual Information. International Journal of Computer Vision 24(2), 137–154 (1997)CrossRefGoogle Scholar
  17. [17]
    West, J., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Comp. Assist. Tomography 21, 554–566 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • S. Ourselin
    • 1
  • A. Roche
    • 1
  • S. Prima
    • 1
  • N. Ayache
    • 1
  1. 1.INRIA Sophia - Epidaure ProjectSophia Antipolis CedexFrance

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