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Point Similarity Measures Based on MRF Modeling of Difference Images for Spline-Based 2D-3D Rigid Registration of X-Ray Fluoroscopy to CT Images

  • Guoyan Zheng
  • Xuan Zhang
  • Slavica Jonić
  • Philippe Thévenaz
  • Michael Unser
  • Lutz-Peter Nolte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)

Abstract

One of the main factors that affect the accuracy of intensity-based registration of two-dimensional (2D) X-ray fluoroscopy to three-dimensional (3D) CT data is the similarity measure, which is a criterion function that is used in the registration procedure for measuring the quality of image match. This paper presents a unifying framework for rationally deriving point similarity measures based on Markov random field (MRF) modeling of difference images which are obtained by comparing the reference fluoroscopic images with their associated digitally reconstructed radiographs (DRR’s). The optimal solution is defined as the maximum a posterior (MAP) estimate of the MRF. Three novel point similarity measures derived from this framework are presented. They are evaluated using a phantom and a human cadaveric specimen. Combining any one of the newly proposed similarity measures with a previously introduced spline-based registration scheme, we develop a fast and accurate registration algorithm. We report their capture ranges, converging speeds, and registration accuracies.

Keywords

Similarity Measure Mutual Information Difference Image Markov Random Field Observation Model 
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.

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References

  1. 1.
    Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparison of similarity measures for use in 2D-3D medical image registration. IEEE T Med. Imaging 17(4), 586–595 (1998)CrossRefGoogle Scholar
  2. 2.
    Lemieux, L., Jagoe, R., Fish, D.R., Kitchen, N.D., Thomas, D.G.T.: A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. Med. Phys. 21(11), 1749–1760 (1994)CrossRefGoogle Scholar
  3. 3.
    Buzug, T.M., Weese, J., Fassnacht, C., Lorenz, C.: Image registration: convex weighting functions for histogram-based similarity measures. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 203–212. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Weese, J., Buzug, T.M., Lorenz, C., Fassnacht, C.: An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images. In: Troccaz, J., Mösges, R., Grimson, W.E.L. (eds.) CVRMed-MRCAS 1997, CVRMed 1997, and MRCAS 1997. LNCS, vol. 1205, pp. 119–128. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multi-modality image registration by maximization of mutual information. IEEE T Med. Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  6. 6.
    Brown, L.M.G., Boult, T.E.: Registration of planar film radiographs with computed tomography. In: IEEE Proceedings of MMBIA, pp. 42–51 (1996)Google Scholar
  7. 7.
    Jonić, S., Thévenaz, P., Zheng, G., Nolte, L.-P., Unser, M.: An Optimized Spline-based registration of a 3D CT to a set of C-arm images. International Journal of Biomedical Imaging (in press, 2006)Google Scholar
  8. 8.
    Jonić, S., Thévenaz, P., Unser, M.: Multiresolution-based registration of a volume to a set of its projection. In: Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing (MI 2003), San Diego CA, USA, Part II, vol. 5032, pp. 1049–1052 (2003)Google Scholar
  9. 9.
    Li, S.Z.: Markov random field modeling in computer vision. Springer, Heidelberg (1995)Google Scholar
  10. 10.
    Zheng, G., Zhang, X., Nolte, L.-P.: Assessing spline-based multi-resolution 2D-3D image registration for practical use in surgical guidance. In: Yang, G.-Z., Jiang, T. (eds.) MIAR 2004. LNCS, vol. 3150, pp. 294–301. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Zöllei, L., Grimson, E., Norbash, A., Wells, W.: 2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators. In: Proceedings of CVPR 2001, vol. 2, pp. 679–703 (2001)Google Scholar
  12. 12.
    Russakoff, D.B., Rohlfind, T., Maurer Jr., C.R.: Fast intensity-based 2D-3D image registration of clinical data using lighting fields. In: Proceedings of ICCV 2003, pp. 416–422 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guoyan Zheng
    • 1
  • Xuan Zhang
    • 1
  • Slavica Jonić
    • 2
    • 3
  • Philippe Thévenaz
    • 2
  • Michael Unser
    • 2
  • Lutz-Peter Nolte
    • 1
  1. 1.MEM Research CenterUniversity of BernBernSwitzerland
  2. 2.Biomedical Imaging Group, École polytechnique fédérale de Lausanne (EPFL)Lausanne VDSwitzerland
  3. 3.Institut de Minéralogie et de Physique des Milieux CondensésUniversité Pierre et Marie CurieParisFrance

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