Skip to main content

Cross-Manifold Guidance in Deformable Registration of Brain MR Images

  • Conference paper
  • First Online:
Book cover Medical Imaging and Augmented Reality (MIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9805))

Included in the following conference series:

Abstract

Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans. Med. Imaging 21, 1421–1439 (2002)

    Article  Google Scholar 

  2. Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. Int. J. Comput. Vis. 24, 137–154 (1997)

    Article  Google Scholar 

  3. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999)

    Article  Google Scholar 

  4. Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61, 139–157 (2005)

    Article  Google Scholar 

  5. Paquin, D., Levy, D., Schreibmann, E., Xing, L.: Multiscale image registration. Math. Biosci. Eng. 3, 389–418 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  6. Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008)

    Article  Google Scholar 

  7. Maes, F., Vandermeulen, D., Suetens, P.: Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Med. Image Anal. 3, 373–386 (1999)

    Article  Google Scholar 

  8. Jia, H., Yap, P.-T., Shen, D.: Iterative multi-atlas-based multi-image segmentation with treebased registration. Neuroimage 59, 422–430 (2012)

    Article  Google Scholar 

  9. Wang, Q., Kim, M., Shi, Y., Wu, G., Shen, D.: Predict brain MR image registration via sparse learning of appearance and transformation. Med. Image Anal. 20, 61–75 (2015)

    Article  Google Scholar 

  10. Aljabar, P., Wolz, R., Rueckert, D.: Manifold learning for medical image registration, segmentation, and classification. In: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. IGI Global (2012)

    Google Scholar 

  11. Ye, D.H., Hamm, J., Kwon, D., Davatzikos, C., Pohl, K.M.: Regional manifold learning for deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 131–138. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62, 782–790 (2012)

    Article  Google Scholar 

  13. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45, S61–S72 (2009)

    Article  Google Scholar 

  14. Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dinggang Shen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, J., Wang, Q., Wu, G., Shen, D. (2016). Cross-Manifold Guidance in Deformable Registration of Brain MR Images. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds) Medical Imaging and Augmented Reality. MIAR 2016. Lecture Notes in Computer Science(), vol 9805. Springer, Cham. https://doi.org/10.1007/978-3-319-43775-0_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-43775-0_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43774-3

  • Online ISBN: 978-3-319-43775-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics