Skip to main content

Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7359)

Abstract

For image registration to be applicable in a clinical setting, it is important to know the degree of uncertainty in the returned point-correspondences. In this paper, we propose a data-driven method that allows one to visualize and quantify the registration uncertainty through spatially adaptive confidence regions. The method applies to various parametric deformation models and to any choice of the similarity criterion. We adopt the B-spline model and the negative sum of squared differences for concreteness. At the heart of the proposed method is a novel shrinkage-based estimate of the distribution on deformation parameters. We present some empirical evaluations of the method in 2-D using images of the lung and liver, and the method generalizes to 3-D.

Keywords

  • Image Registration
  • Coverage Rate
  • Deformation Model
  • Registration Error
  • Baseline Covariance

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.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (Canada)
  • 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 

  2. Chun, S.Y., Fessler, J.: A simple regularizer for B-spline nonrigid image registration that encourages local invertibility. IEEE J. Sel. Top. Sig. Proc. 3(1), 159–169 (2009); special Issue on Digital Image Processing Techniques for Oncology

    Google Scholar 

  3. Fitzpatrick, J.M., West, J.B.: The distribution of target registration error in rigid-body, point-based registration. IEEE Trans. Med. Imaging 20(9), 917–927 (2001)

    CrossRef  Google Scholar 

  4. Golub, G.H., Van Loan, C.F.: Matrix computations, 3rd edn. Johns Hopkins University Press (1996)

    Google Scholar 

  5. Holden, M.: A review of geometric transformations for nonrigid body registration. IEEE Trans. Med. Imag. 27(1), 111–128 (2008)

    CrossRef  Google Scholar 

  6. Hub, M., Kessler, M.L., Karger, C.P.: A stochastic approach to estimate the uncertainty involved in B-spline image registration. IEEE Trans. Med. Imaging 28(11), 1708–1716 (2009)

    CrossRef  Google Scholar 

  7. Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Transactions on Image Processing 12(11), 1427–1442 (2003)

    CrossRef  Google Scholar 

  8. Kybic, J.: Bootstrap resampling for image registration uncertainty estimation without ground truth. IEEE Transactions on Image Processing 19(1), 64–73 (2010)

    CrossRef  MathSciNet  Google Scholar 

  9. Ledoit, O., Wolf, M.: Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection. Journal of Empirical Finance 10, 603–621 (2003)

    CrossRef  Google Scholar 

  10. Risholm, P., Pieper, S., Samset, E., Wells III, W.M.: Summarizing and Visualizing Uncertainty in Non-rigid Registration. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 554–561. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  11. Robinson, M.D., Milanfar, P.: Fundamental performance limits in image registration. IEEE Transactions on Image Processing 13(9), 1185–1199 (2004)

    CrossRef  Google Scholar 

  12. Simpson, I.J., Schnabel, J.A., Groves, A.R., Andersson, J.L., Woolrich, M.W.: Probabilistic inference of regularisation in non-rigid registration. NeuroImage 59(3), 2438–2451 (2012)

    CrossRef  Google Scholar 

  13. Yetik, I.S., Nehorai, A.: Performance bounds on image registration. IEEE Transactions on Signal Processing 54(5), 1737–1749 (2006)

    CrossRef  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

Watanabe, T., Scott, C. (2012). Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31340-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31339-4

  • Online ISBN: 978-3-642-31340-0

  • eBook Packages: Computer ScienceComputer Science (R0)