Skip to main content

Template Estimation for Large Database: A Diffeomorphic Iterative Centroid Method Using Currents

  • Conference paper
Book cover Geometric Science of Information (GSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8085))

Included in the following conference series:

Abstract

Computing a template in the Large Deformation Diffeomorphic Metric Mapping framework is a key step for the shape analysis of anatomical structures, but can lead to very computationally expensive algorithms in the case of large databases. We present an iterative method which quickly provides a centroid of the population in shape space. This centroid can be used as a rough template estimate or as initialization for template estimation methods.

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Grenander, U., Miller, M.I.: Computational anatomy: An emerging discipline. Quarterly of Applied Mathematics 56(4), 617–694 (1998)

    MathSciNet  MATH  Google Scholar 

  2. Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing 5(10), 1435–1447 (1996)

    Article  Google Scholar 

  3. Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)

    Article  Google Scholar 

  4. Ma, J., Miller, M.I., Trouvé, A., Younes, L.: Bayesian template estimation in computational anatomy. NeuroImage 42(1), 252–261 (2008)

    Article  Google Scholar 

  5. Glaunès, J., Joshi, S.: Template estimation from unlabeled point set data and surfaces for computational anatomy. In: Pennec, X., Joshi, S. (eds.) Proc. of the International Workshop on the Mathematical Foundations of Computational Anatomy (MFCA 2006), pp. 29–39 (October 1, 2006)

    Google Scholar 

  6. Durrleman, S., Pennec, X., Trouvé, A., Ayache, N., et al.: A forward model to build unbiased atlases from curves and surfaces. In: 2nd Medical Image Computing and Computer Assisted Intervention. Workshop on Mathematical Foundations of Computational Anatomy, pp. 68–79 (2008)

    Google Scholar 

  7. Durrleman, S., Prastawa, M., Korenberg, J.R., Joshi, S., Trouvé, A., Gerig, G.: Topology preserving atlas construction from shape data without correspondence using sparse parameters. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 223–230. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Vaillant, M., Glaunès, J.: Surface matching via currents. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 381–392. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Glaunes, J.: Transport par difféomorphismes de points, de mesures et de courants pour la comparaison de formes et l’anatomie numérique. PhD thesis, Université Paris 13 (2005)

    Google Scholar 

  10. Yang, X., Goh, A., Qiu, A.: Approximations of the diffeomorphic metric and their applications in shape learning. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 257–270. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  11. Tenenbaum, J., Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  12. Arnaudon, M., Nielsen, F.: On approximating the riemannian 1-center. Computational Geometry 46(1), 93–104 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chupin, M., Hammers, A., Liu, R.S.N., Colliot, O., Burdett, J., Bardinet, E., Duncan, J.S., Garnero, L., Lemieux, L.: Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation. NeuroImage 46(3), 749–761 (2009)

    Article  Google Scholar 

  14. Durrleman, S., Pennec, X., Trouvé, A., Ayache, N.: Statistical models of sets of curves and surfaces based on currents. Medical Image Analysis 13(5), 793–808 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cury, C., Glaunès, J.A., Colliot, O. (2013). Template Estimation for Large Database: A Diffeomorphic Iterative Centroid Method Using Currents. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2013. Lecture Notes in Computer Science, vol 8085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40020-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40020-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40019-3

  • Online ISBN: 978-3-642-40020-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics