Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Precise Registration of CT and MR Volumes Based on a New Creaseness Measure

  • Conference paper
Noblesse Workshop on Non-Linear Model Based Image Analysis

Abstract

Image registration or matching attempts to solve the problem that arises when two images taken at different times, by different sensors or from different viewpoints need to be compared. An upcoming application of image registration is in the field of medical images, specially since the introduction of 3-D modalities. Many methods have been proposed for multi-sensor medical image registration [1]. Our research has focused in CT-MR registration because these modalities are widely available and they provide complementary information: CT depicts accurately bones, while MR distinguishes soft tissues.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

Similar content being viewed by others

References

  1. J. B. A. Maintz, M. A. Viergever. A survey of medical image registration. Medical Image Analysis, Vol 2, No. 1, 1–36, 1998.

    Google Scholar 

  2. P. A. van den Elsen, J. B. A. Maintz, E-J. D. Pol, and M. Viergever. Automatic registration of CT and MR brain images using correlation of geometrical features. IEEE Trans. on Medical Imaging, 14:384–396, 1995.

    Article  Google Scholar 

  3. D. Eberly, R. Gardner, B. Morse, S. Pizer, and C. Scharlach. Ridges for image analysis. J. of Mathematical Imaging and Vision, 4:353–373, 1994.

    Article  Google Scholar 

  4. A. López and J. Serrat and F. Lumbreras. Creaseness from Levet Set Extrinsic Curvature. Accepted in the 5th Euro. Conf. on Computer Vision, Proc. published in the series LNCS. Springer-Verlag, 1998.

    Google Scholar 

  5. C. Studholme, D. Hill and D.J. Hawkes. Automated 3D registration of MR and CT images of the head. Medical Image Analysis 1(2): 163–175 1996

    Article  Google Scholar 

  6. J. West., J.M. Fitzpatrick, M.Y. Yang et al. Comparison and evaluation of retrospective intermodality brain image registration techniques Journal of Computed Assisted Tomography, Vol. 21, pp. 554–566, 1997.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag London Limited

About this paper

Cite this paper

Lloret, D., López, A.M., Serrat, J. (1998). Precise Registration of CT and MR Volumes Based on a New Creaseness Measure. In: Marshall, S., Harvey, N.R., Shah, D. (eds) Noblesse Workshop on Non-Linear Model Based Image Analysis. Springer, London. https://doi.org/10.1007/978-1-4471-1597-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-1597-7_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-76258-4

  • Online ISBN: 978-1-4471-1597-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics