Skip to main content

Fast Mesh-Based Medical Image Registration

  • Conference paper
Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8888))

Included in the following conference series:

Abstract

In this paper a fast triangular mesh based registration method is proposed. Having Template and Reference images as inputs, the template image is triangulated using a content adaptive mesh generation algorithm. Considering the pixel values at mesh nodes, interpolated using spline interpolation method for both of the images, the energy functional needed for image registration is minimized. The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in comparison to the pixel-wise registration method, can be solved directly. Mean Squared Difference (MSD) is used as a metric for evaluating the results. Using the mesh based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.

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 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.

References

  1. Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal image analysis: concepts, applications and potential. Prog. Retin. Eye Res. 25(1), 99–127 (2006)

    Article  Google Scholar 

  2. Baghaie, A., Yu, Z.: Curvature-Based Registration for Slice Interpolation of Medical Images. In: Zhang, Y.J., Tavares, J.M.R.S. (eds.) CompIMAGE 2014. LNCS, vol. 8641, pp. 69–80. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  3. Zitova, B., Flusser, J.: Image registration methods: a survey. Image Vision Comput. 21(11), 977–1000 (2003)

    Article  Google Scholar 

  4. Modersitzki, J.: Numerical methods for image registration. OUP, Oxford (2003)

    Google Scholar 

  5. Fischer, B., Modersitzki, J.: A unified approach to fast image registration and a new curvature based registration technique. Linear Algebra Appl. 380, 107–124 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  6. Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: A survey. IEEE T. Med. Imaging 32(7), 1153–1190 (2013)

    Article  Google Scholar 

  7. Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE T. Med. Imaging 17(4), 586–595 (1998)

    Article  Google Scholar 

  8. Fluck, O., Vetter, C., Wein, W., Kamen, A., Preim, B., Westermann, R.: A survey of medical image registration on graphics hardware. Comput. Meth. Prog. Bio. 104(3), e45–e57 (2011)

    Google Scholar 

  9. Corvi, M., Nicchiotti, G.: Multiresolution image registration. In: EEE International Conference on Image Processing 1995, vol. 3, pp. 224–227. IEEE Press (1995)

    Google Scholar 

  10. Haber, E., Heldmann, S., Modersitzki, J.: Adaptive mesh refinement for nonparametric image registration. SIAM J. Sci. Comput. 30(6), 3012–3027 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  11. Popuri, K., Cobzas, D., Jagersand, M.: Fast FEM-based non-rigid registration. In: Canadian Conference on Computer and Robot Vision (CRV) 2010. IEEE Press (2010)

    Google Scholar 

  12. Xu, M., Gao, Z., Yu, Z.: Feature-Sensitive and Adaptive Mesh Generation of Grayscale Images. In: Zhang, Y.J., Tavares, J.M.R.S. (eds.) CompIMAGE 2014. LNCS, vol. 8641, pp. 204–215. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  13. Xu, G.: Convergent discrete Laplace-Beltrami operators over triangular surfaces. In: Geometric Modeling and Processing 2004. IEEE Press (2004)

    Google Scholar 

  14. Desbrun, M., Meyer, M., Schrder, P., Barr, A.H.: Implicit fairing of irregular meshes using diffusion and curvature flow. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 317–324. ACM Press/Addison-Wesley Publishing Co. (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Baghaie, A., Yu, Z., D’souza, R.M. (2014). Fast Mesh-Based Medical Image Registration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14364-4_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14363-7

  • Online ISBN: 978-3-319-14364-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics