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Random Walks with Efficient Search and Contextually Adapted Image Similarity for Deformable Registration

  • Lisa Y. W. Tang
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

We develop a random walk-based image registration method [1] that incorporates two novelties: 1) a progressive optimization scheme that conducts the solution search efficiently via a novel use of information derived from the obtained probabilistic solution, and 2) a data-likelihood re-weighting step that contextually performs feature selection in a spatially adaptive manner so that the data costs are based primarily on trusted information sources. Synthetic experiments on three public datasets of different anatomical regions and modalities showed that our method performed efficient search without sacrificing registration accuracy. Experiments performed on 60 real brain image pairs from a public dataset also demonstrated our method’s better performance over existing non-probabilistic image registration methods.

Keywords

Search Space Image Registration Discretization Error Registration Error Registration Accuracy 
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.
    Cobzas, D., Sen, A.: Random walks for deformable image registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 557–565. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622–639 (2011)CrossRefGoogle Scholar
  3. 3.
    Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Image Anal. 12(6), 731–741 (2008)CrossRefGoogle Scholar
  4. 4.
    Heinrich, M.P., Jenkinson, M., Brady, S.M., Schnabel, J.A.: Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 115–122. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Tang, L., Hero, A., Hamarneh, G.: Locally-adaptive similarity metric for deformable medical image registration. In: ISBI, pp. 728–731 (2012)Google Scholar
  6. 6.
    Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., Navab, N.: Optical flow estimation with uncertainties through dynamic MRFs. In: CVPR (2008)Google Scholar
  7. 7.
    Bagon, S., Galun, M.: A unified multiscale framework for discrete energy minimization. CoRR, vol. 4867 (2012)Google Scholar
  8. 8.
    Kohli, P., Torr, P.: Measuring uncertainty in graph cut solutions. Comput. Vis. Image Und. 112(1), 30–38 (2008)CrossRefGoogle Scholar
  9. 9.
    Li, Y., Verma, R.: Multichannel image registration by feature-based information fusion. IEEE Trans. Med. Imag. 30(3), 707–720 (2011)CrossRefGoogle Scholar
  10. 10.
    Skerl, D., Likar, B., Pernus, F.: A protocol for evaluation of similarity measures for non-rigid registration. Med. Image Anal. 12(1), 42–54 (2008)CrossRefGoogle Scholar
  11. 11.
    Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watershed: A unifying graph-based optimization framework. IEEE Trans. Pattern Anal. Mach. Intell. 33(7), 1384–1399 (2011)CrossRefGoogle Scholar
  12. 12.
    Lempitsky, V., Rother, C., Roth, S., Blake, A.: Fusion moves for markov random field optimization. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1392–1405 (2010)CrossRefGoogle Scholar
  13. 13.
    Klein, A., Andersson, J., Ardekani, B., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G., Collins, D., Gee, J., Hellier, P., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)CrossRefGoogle Scholar
  14. 14.
    Heinrich, M., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F., Brady, M., Schnabel, J.: MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Med. Image Anal. 16(7), 1423–1435 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lisa Y. W. Tang
    • 1
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis Lab.Simon Fraser UniversityBurnabyCanada

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