Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 90–97Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration for MR Brain Images

Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration for MR Brain Images

  • Guorong Wu19,
  • Minjeong Kim19,
  • Qian Wang19 &
  • …
  • Dinggang Shen19 
  • Conference paper
  • 4007 Accesses

  • 7 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

Deformable registration has been widely used in neuroscience studies for spatial normalization of brain images onto a standard space. Due to high anatomical variances across individual brains, registration performance could be limited when trying to estimate entire deformation pathway either from template to subject or subject to template. Symmetric image registration offers an effective way to simultaneously deform template and subject images towards each other until they meet at the middle point. Although some intensity-based registration algorithms have nicely incorporated this concept of symmetric deformation, the intensity matching between two images may not necessarily imply the correct matching of anatomical correspondences. In this paper, we integrate both strategies of hierarchical attribute matching and symmetric diffeomorphic deformation for building a new symmetric-diffeomorphic registration algorithm for MR brain images. The performance of our proposed method has been extensively evaluated and further compared with top-ranked image registration methods (SyN and diffeomorphic Demons) on brain MR images. In all experiments, our registration method achieves the best registration performance, compared to all other state-of-the-art registration methods.

Keywords

  • Image Registration
  • Deformation Field
  • Attribute Vector
  • Registration Method
  • Deformable Image Registration

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.

Download conference paper PDF

References

  1. Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998)

    CrossRef  Google Scholar 

  2. 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)

    CrossRef  Google Scholar 

  3. 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. Medical Image Analysis 12, 26–41 (2008)

    CrossRef  Google Scholar 

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

    Google Scholar 

  5. Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W.: Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39, 1064–1080 (2008)

    CrossRef  Google Scholar 

  6. Christensen, G.E., Geng, X., Kuhl, J.G., Bruss, J., Grabowski, T.J., Pirwani, I.A., Vannier, M.W., Allen, J.S., Damasio, H.: Introduction to the Non-rigid Image Registration Evaluation Project (NIREP). In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds.) WBIR 2006. LNCS, vol. 4057, pp. 128–135. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  7. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 567–585 (1989)

    CrossRef  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radioloy and BRIC, Univerity of North Carolina at Chapel Hill, USA

    Guorong Wu, Minjeong Kim, Qian Wang & Dinggang Shen

Authors
  1. Guorong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minjeong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dinggang Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, G., Kim, M., Wang, Q., Shen, D. (2012). Hierarchical Attribute-Guided Symmetric Diffeomorphic Registration for MR Brain Images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_12

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33418-4_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature