Skip to main content

Phase Contrast MRI Segmentation Using Velocity and Intensity

  • Conference paper
Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

Included in the following conference series:

Abstract

This paper presents a method for three-dimensional (3D) segmentation of blood vessels, i.e. determining the surface of the vessel wall, using a combination of velocity data and magnitude images obtained using phase contrast MRI. In addition to standard MRI images, phase contrast MRI gives velocity information for blood and tissue in the human body. The proposed method uses a variational formulation of the segmentation problem which combines different cues; velocity and magnitude. The segmentation is performed using the level set method. Experiments on phantom data and clinical data support the proposed method.

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. Frangi, A., Niessen, W., Viergever, M.: Three-dimensional modeling for functional analysis of cardiac images: A review. IEEE Trans. on Medical Imaging 20 (2001)

    Google Scholar 

  2. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man and Cybernetics 9, 62–66 (1979)

    Article  Google Scholar 

  3. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Computer Vision 1, 321–331 (1987)

    Article  Google Scholar 

  4. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. Journal of Computer Vision (1997)

    Google Scholar 

  5. Osher, S.J., Fedkiw, R.P.: Level Set Methods and Dynamic Implicit Surfaces. Springer, Heidelberg (2002)

    Google Scholar 

  6. Sethian, J.: Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry. In: Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, Cambridge (1999)

    Google Scholar 

  7. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics 79, 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  8. Kimmel, R., Bruckstein, A.: Regularized laplacian zero crossings as optimal edge integrators. Int. Journal of Computer Vision 53, 225–243 (2003)

    Article  Google Scholar 

  9. Ebbers, T.: Cardiovascular Fluid Dynamics. PhD thesis, Departments of Biomedical Engineering & Medicine and Care, Linkoping University, Sweden (2001)

    Google Scholar 

  10. Wong, A., Liu, H., Shi, P.: Segmentation of myocardium using velocity field constrained front propagation. In: IEEE Applications of Computer Vision (2002)

    Google Scholar 

  11. Rousson, M., Deriche, R.: A variational framework for active and adaptive segmentation of vector valued images. Technical Report 4512, INRIA (2002)

    Google Scholar 

  12. Lorigo, L., Faugeras, O., Grimson, W., Keriven, R., Kikinis, R., Nabavi, A., Westin, C.F.: Curves: Curve evolution for vessel segmentation. IEEE Transactions on Medical Image Analysis 5, 195–206 (2001)

    Article  Google Scholar 

  13. Vasilevskiy, A., Siddiqi, K.: Flux maximizing geometric flows. IEEE Trans. on PAMI 24, 1565–1578 (2002)

    Google Scholar 

  14. Descoteaux, M., Collins, L., Siddiqi, K.: Geometric flows for segmenting vasculature in MRI: Theory and validation. In: MICCAI, France (2004)

    Google Scholar 

  15. Solem, J., Persson, M., Heyden, A.: Velocity based segmentation in phase contrast mri images. In: MICCAI, France (2004)

    Google Scholar 

  16. Pelc, N.J., Herfkens, R.J., Shimakawa, A., Enzmann, D.: Phase contrast cine magnetic resonance imaging. Magnetic Resonance Quarterly 4, 229–254 (1991)

    Google Scholar 

  17. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proc. Alvey Conf., pp. 189–192 (1988)

    Google Scholar 

  18. Zhao, H., Chan, T., Merriman, B., Osher, S.: A variational level set approach to multiphase motion. J. Computational Physics 127, 179–195 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  19. Paragios, N.: A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Transactions on Medical Imaging 22, 773–776 (2003)

    Article  Google Scholar 

  20. Overgaard, N., Solem, J.: An analysis of variational alignment of curves in images. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 480–491. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  21. Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  22. Cremers, D.: Statistical Shape Knowledge in Variational Image Segmentation. PhD thesis, Dept. of Mathematics and Computer Science, University of Mannheim, Germany (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Persson, M., Solem, J.E., Markenroth, K., Svensson, J., Heyden, A. (2005). Phase Contrast MRI Segmentation Using Velocity and Intensity. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_11

Download citation

  • DOI: https://doi.org/10.1007/11408031_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25547-5

  • Online ISBN: 978-3-540-32012-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics