Advertisement

Robust Multi-scale Non-rigid Registration of 3D Ultrasound Images

  • Ioannis Pratikakis
  • Christian Barillot
  • Pierre Hellier
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)

Abstract

In this paper, we embed the minimization scheme of an automatic 3D non-rigid registration method in a multi-scale framework. The initial model formulation was expressed as a robust multiresolution and multigrid minimization scheme. At the finest level of the multiresolution pyramid, we introduce a focusing strategy from coarse-to-fine scales which leads to an improvement of the accuracy in the registration process. A focusing strategy has been tested for a linear and a non-linear scale-space. Results on 3D Ultrasound images are discussed.

Keywords

Mean Square Error Motion Estimation Grid Level Registration Model Multigrid Scheme 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    L. Alvarez, J. Weickert, and J. Sanchez. A scale-space approach to nonlocal optical flow calculations. In Scale-Space’ 99, pages 235–246, 1999.Google Scholar
  2. 2.
    M. Black and A. Rangarajan. On the unification of line processes, outlier rejection and robust statistics with applications in early vision. International Journal of Computer Vision, 19(1):57–91, 1996.CrossRefGoogle Scholar
  3. 3.
    F.L. Bookstein. Principal warps: Thin-plate slines and the decomposition of defomations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567–585, 1989.zbMATHCrossRefGoogle Scholar
  4. 4.
    F. Catté, P.L. Lions, J.M. Morel, and T. Coll. Image selective smoothing and edge detection by nonlinear difusion. SIAM Journal on Numerical analysis, 29:182–193, 1992.MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    P. Hellier, C. Barillot, E. Mémin, and P. Pérez. An energy-based framework for dense 3D registration of volumetric brain images. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), volume II, pages 270–275, Hilton Head Island, South Carolina, USA, June 2000.Google Scholar
  6. 6.
    B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185–203, August 1981.CrossRefGoogle Scholar
  7. 7.
    J. Weber and J. Malik. Robust computation of optical flow in a multi-scale differential framework. International Journal of Computer Vision, 14:67–81, 1995.CrossRefGoogle Scholar
  8. 8.
    E. Mémin and P. Pérez. Dense estimation and object-based segmentation of the optical flow with robust techniques. IEEE Transactions on Image Processing, 7(5):703–719, 1998.CrossRefGoogle Scholar
  9. 9.
    A. Morsy and O. VonRamm. 3D ultrasound tissue motion tracking using correlation search. Ultrasonic Imaging, 20:151–159, 1998.CrossRefGoogle Scholar
  10. 10.
    H.H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:565–593, 1986.CrossRefGoogle Scholar
  11. 11.
    W.J. Niessen, J.S. Duncan, M. Nielsen, L.M.J. Florack, ter Haar Romeny B.M, and M.A. Viergever. A multiscale approach to image sequence analysis. Computer Vision and Image Understanding, 65(2):259–268, 1997.CrossRefGoogle Scholar
  12. 12.
    X. Pennec, P. Cachier, and N. Ayache. Understanding the “demon’s algorithm”: 3D non-rigid registration by gradient descent. In MICCAI, pages 597–605, September 1999.Google Scholar
  13. 13.
    P. Perona and J. Malik. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629–639, 1990.CrossRefGoogle Scholar
  14. 14.
    M. Strintzis and I. Kokkinidis. Maximum likelihood motion estimation in ultrasound image sequences. IEEE Signal Processing Letters, 4(6):156–157, 1997.CrossRefGoogle Scholar
  15. 15.
    A.P. Witkin. Scale-space filtering. In International Joint Conference on Artificial intelligence, pages 1019–1023, Karlsruhe, W. Germany, 1983.Google Scholar
  16. 16.
    F. Yeung, S. Levinson, D. Fu, and K. Parker. Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh. IEEE Transactions on Medical Imaging, 17(6):945–956, 1998.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ioannis Pratikakis
    • 1
  • Christian Barillot
    • 1
  • Pierre Hellier
    • 1
  1. 1.IRISA, INRIA-CNRS, ViSTA ProjectCampus universitaire de BeaulieuRennes CedexFrance

Personalised recommendations