Skip to main content

Intensity-Based 2D-3D Registration Using Normalized Gradient Fields

  • Conference paper
  • First Online:
Book cover Bildverarbeitung für die Medizin 2020

Part of the book series: Informatik aktuell ((INFORMAT))

Zusammenfassung

2D-3D registration is central to image guided minimal invasive endovascular therapies such as the treatment of aneurysms. We propose a novel intensity-based 2D-3D registration method based on digitally reconstructed radiographs and the so-called Normalized Gradient Fields (NGF) as a distance measure. We evaluate our method on publicly available clinical data and compare it to five other state-of-the-art 2D-3D registration methods. The results show that our method achieves better accuracy with comparable results in terms of the number of successful registrations and robustness.

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 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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.

Literatur

  1. Markelj P, Tomaževič D, Likar B, et al. A review of 3D/2D registration methods for image-guided interventions. Med Image Anal. 2012;16(3):642–61.

    Google Scholar 

  2. Goitein M, Abrams M, Rowell D, et al. Multi-dimensional treatment planning: II. Beam’s eye-view, back projection, and projection through CT sections. Int J Radiat Oncol Biol Phys. 1983;9(6):789 – 97.

    Google Scholar 

  3. Russakoff DB, Rohlfing T, Mori K, et al. Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration. IEEE Trans Med Imaging. 2005;24(11):1441–54.

    Google Scholar 

  4. Otake Y, Armand M, Armiger RS, et al. IEEE Trans Med Imaging. 2011;31(4):948–62.

    Google Scholar 

  5. Haber E, Modersitzki J. Intensity gradient based registration and fusion of multimodal images. In: Proc MiCCAI; 2006. p. 726–33.

    Google Scholar 

  6. Rühaak J, Heldmann S, Kipshagen T, et al. Highly accurate fast lung CT registration. In: SPIE Medical Imaging 2013: Image Processing; 2013.

    Google Scholar 

  7. Rühaak J, Polzin T, Heldmann S, et al. Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans Med Imaging. 2017;36(8):1746–57.

    Google Scholar 

  8. Mitrović U, Pernuš F, Špiclin Ž, et al. 3D-2D registration of cerebral angiograms: a method and evaluation on clinical images. IEEE Trans Med Imaging. 2013;32(8):1550–63.

    Google Scholar 

  9. Mitrović U, Likar B, Pernuš F, et al. 3D–2D registration in endovascular imageguided surgery: evaluation of state-of-the-art methods on cerebral angiograms. Int J Comput Assist Radiol Surg. 2018;13(2):193–202.

    Google Scholar 

  10. Van de Kraats EB, Penney GP, Tomazevic D, et al. Standardized evaluation methodology for 2-D-3-D registration. IEEE Trans Med Imaging. 2005;24(9):1177–89.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Annkristin Lange .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lange, A., Heldmann, S. (2020). Intensity-Based 2D-3D Registration Using Normalized Gradient Fields. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_33

Download citation

Publish with us

Policies and ethics