Skip to main content

Evaluation of Medical Image Registration by Using 3D SIFT and Phase-Only Correlation

  • Conference paper
Abdominal Imaging. Computational and Clinical Applications (ABD-MICCAI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7601))

Abstract

An effective method for quantitatively evaluating rigid and non-rigid image registration without any manual assessment is proposed. This evaluation method is based on feature point detection in reference images and corresponding point localization in registered floating images. For feature point detection, a 3D SIFT keypoint detector is applied to determine evaluation reference points in liver vessel regions of reference images. For corresponding point localization, a 3D phase-only correlation approach is applied to match reference points and their corresponding points. Distance between the reference points and the correspondences can be used to estimate image registration errors. With the proposed method, users can evaluate different registration algorithms using their own image data automatically.

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. Hajnal, J.V., Hawkes, D.J., Hill, D.L.G.: Medical Image Registration. CRC Press (2001)

    Google Scholar 

  2. West, J., et al.: Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques. Journal of Computer Assisted Tomography 21(4), 554–566 (1997)

    Article  Google Scholar 

  3. 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)

    Chapter  Google Scholar 

  4. Cheung, W., Hamarneh, G.: N-SIFT: N-dimensional Scale Invariant Feature Transform. IEEE Trans. Image Processing 18(9), 2012–2021 (2009)

    Article  MathSciNet  Google Scholar 

  5. Tajima, Y., et al.: High-accuracy Volume Registration based on 3D Phase-only Correlation. IEICE Trans. Information and Systems J94-D(8), 1398–1409 (2011)

    Google Scholar 

  6. Charnoz, A., Agnus, V., Soler, L.: Portal Vein Registration for the Follow-Up of Hepatic Tumours. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004, Part I. LNCS, vol. 3216, pp. 878–886. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Matsuzaki, K., et al.: Development of Computer-aided Diagnosis System using Intelligent Process Manager of Multiple Image Processing Algorithms for Full Automated Detection of Tumors. To be appeared in: CARS 2012 (2012)

    Google Scholar 

  8. Sato, Y., et al.: Tissue Classification based on 3D Local Intensity Structures for Volume Rendering. Transactions on Visualization and Computer Graphics 6(2), 160–180 (2000)

    Article  Google Scholar 

  9. Lowe, D.G.: Distinctive Image Features from Scale-invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  10. Takita, K., Muquit, M.A., Aoki, T., Higuchi, T.: A Sub-pixel Correspondence Search Technique for Computer Vision Applications. IEICE Trans. Fundamentals E87-A(8), 1913–1922 (2004)

    Google Scholar 

  11. Klein, S., et al.: Elastix: A Toolbox for Intensity-based Medical Image Registration. IEEE Trans. Medical Imaging 29(1), 196–205 (2010)

    Article  Google Scholar 

  12. Li, Z., et al.: Efficient Rigid Registration for Medical Images Based on Small Sample Set. IEICE Technical Report, MI 111(199), 1–6 (2011)

    Google Scholar 

  13. Miyazawa, K., et al.: A Novel Approach for Volume Registration using 3D Phase-Only Correlation. In: Radiological Society of North America (RSNA) 2009, p. 1070 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, Z., Kurihara, T., Matsuzaki, K., Irie, T. (2012). Evaluation of Medical Image Registration by Using 3D SIFT and Phase-Only Correlation. In: Yoshida, H., Hawkes, D., Vannier, M.W. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2012. Lecture Notes in Computer Science, vol 7601. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33612-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33612-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33611-9

  • Online ISBN: 978-3-642-33612-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics