Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

  • Lisa Tang
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6204)


We propose a reliability measure that identifies informative image cues useful for registration, and present a novel, data-driven approach to spatially adapt regularization to the local image content via use of the proposed measure. We illustrate the generality of this adaptive regularization approach within a powerful discrete optimization framework and present various ways to construct a spatially varying regularization weight based on the proposed measure. We evaluate our approach within the registration process using synthetic experiments and demonstrate its utility in real applications. As our results demonstrate, our approach yielded higher registration accuracy than non-adaptive approaches and the proposed reliability measure performed robustly even in the presences of noise and intensity inhomogenity.


Image Registration Markov Random Field Intensity Inhomogenity Deformable Registration Adaptive Regularization 
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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  1. 1.
    Glocker, et al.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)CrossRefGoogle Scholar
  2. 2.
    Tang, T., Chung, A.: Non-rigid image registration using graph-cuts. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 916–924. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Kwon, D., et al.: Nonrigid image registration using dynamic higher-order MRF model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 373–386. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Hellier, et al.: Hierarchical estimation of a dense deformation field for 3-d robust registration. IEEE Trans. Med. Imaging 20(5), 388–402 (2001)CrossRefGoogle Scholar
  5. 5.
    Luan, et al.: Multimodality image registration by maximization of quantitative-qualitative measure of mutual information. Pattern Recognit. 41, 285–298 (2008)zbMATHCrossRefGoogle Scholar
  6. 6.
    Lester, et al.: Non-linear registration with the variable viscosity fluid algorithm. In: IEEE IPMI, pp. 238–251 (1999)Google Scholar
  7. 7.
    Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comput. Vision Image Understand. 66, 207–222 (1997)CrossRefGoogle Scholar
  8. 8.
    Rexilius, J., Warfield, S., Guttmann, C.: A novel nonrigid registration algorithm and applications. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 923–931. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Kabus, S.: Multiple-Material Variational Image Registration. PhD thesis, der Universitat zu Lubeck (October 2006)Google Scholar
  10. 10.
    Pitiot, A., Guimond, A.: Geometrical regularization of displacement fields for histological image registration. Med. Image Anal. 12(1), 16–25 (2008)CrossRefGoogle Scholar
  11. 11.
    Stefanescu, et al.: Grid powered nonlinear image registration with locally adaptive regularization. Med. Image Anal. 8(3), 325–342 (2004)CrossRefGoogle Scholar
  12. 12.
    Suarez, et al.: Nonrigid registration using regularized matching weighted by local structure. In: MICCAI, pp. 581–589 (2003)Google Scholar
  13. 13.
    Ishikawa, H.: Exact optimization for markov random fields with convex priors. IEEE Trans. Pattern Anal. Mach. Intell. 25(10), 1333–1336 (2003)CrossRefGoogle Scholar
  14. 14.
    Komodakis, et al.: Performance vs computational efficiency for optimizing single and dynamic MRFs. Comput. Vision Image Understand. 112(1), 14–29 (2008)CrossRefGoogle Scholar
  15. 15.
    Shekhovtsov, A., Kovtun, I., Hlavac, V.: Efficient MRF deformation model for non-rigid image matching. Comput. Vision Image Understand 112, 91–99 (2008)CrossRefGoogle Scholar
  16. 16.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)CrossRefGoogle Scholar
  17. 17.
    Glocker, et al.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)CrossRefGoogle Scholar
  18. 18.
    Paquin, D., Levy, D., Xing, L.: Multiscale deformable registration of noisy medical images. Math. Biosc. Engin. 5(1), 125–144 (2008)zbMATHMathSciNetGoogle Scholar
  19. 19.
    Rao, J., et al.: Adaptive contextual energy parameterization for automated image segmentation. In: Bebis, G. (ed.) ISVC 2009. LNCS, vol. 5875, pp. 1089–1100. Springer, Heidelberg (2009)Google Scholar
  20. 20.
    Donias, M., Baylou, P., Keskes, N.: Curvature of oriented patterns: 2-d and 3-d estimation from differential geomery. In: ICIP, vol. 70, pp. 236–240 (1998)Google Scholar
  21. 21.
    Lindeberg, T.: On scale selection for differential operators. In: Proc. 8th Scandinavian Conf. on Image Analysis, pp. 857–866 (1993)Google Scholar
  22. 22.
    Leung, et al.: Contour and texture analysis for image segmentation. Int. J. Comput. Vision 43(1), 7–27 (2001)zbMATHCrossRefGoogle Scholar
  23. 23.
    Kwan, D., et al.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imaging 18(11), 1085–1097 (1999)CrossRefGoogle Scholar
  24. 24.
    Zhang, L., Seitz, S.: Estimating optimal parameters for MRF stereo from a single image pair. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 331–342 (2007)CrossRefGoogle Scholar
  25. 25.
    Hamarneh, G., et al.: Simulation of ground-truth validation data via physically- and statistically-based warps. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 459–467. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  26. 26.
    Klein, et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lisa Tang
    • 1
  • Ghassan Hamarneh
    • 1
  • Rafeef Abugharbieh
    • 2
  1. 1.Medical Image Analysis Lab., School Computing ScienceSimon Fraser University 
  2. 2.Biomedical Signal and Image Computing Lab., Department of Electrical and Computer EngineeringUniversity of British Columbia 

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