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Shape-based segmentation and tracking in 4D cardiac MR images

  • Daniel Rueckert
  • Peter Burger
Segmentation and Deformable Models
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1205)

Abstract

We present a new approach to shape-based segmentation and tracking of multiple, deformable anatomical structures in cardiac MR images. We propose to use an energy-minimizing geometrically deformable template (GDT) which can deform into similar shapes under the influence of image forces. The degree of deformation of the template from its equilibrium shape is measured by a penalty function associated with mapping between the two shapes. In 2D, this term corresponds to the bending energy of an idealized thin-plate of metal. By minimizing this term along with the image energy terms of the classic deformable model, the deformable template is attracted towards objects in the image whose shape is similar to its equilibrium shape. This framework allows the simultaneous segmentation of multiple deformable objects using intra- as well as inter-shape information. The energy minimization problem of the deformable template is formulated in a Bayesian framework and solved using relaxation techniques: Simulated Annealing (SA), a stochastic relaxation technique is used for segmentation while Iterated Conditional Modes (ICM), a deterministic relaxation technique is used for tracking. We present results of the algorithm applied to the reconstruction of the left and right ventricle of the human heart in 4D MR images.

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References

  1. 1.
    E. Bardinet, L. D. Cohen, and N. Ayache. Tracking and motion analysis of the left ventricle with deformable superquadrics. Medical Image Analysis, 1(2), 1996.Google Scholar
  2. 2.
    J. Park, D. Metaxas, and L. Axel. Analysis of left ventricular wall motion based on volumetric deformable models and MRI-SPAMM. Medical Image Analysis 1(1):53–71, 1996.CrossRefPubMedGoogle Scholar
  3. 3.
    C. Nastar and N. Ayache. Classification of nonridgid motion in 3D images using physics-based vibration analysis. In IEEE Workshop on Biomedical Image Analysis, pages 61–69, 1994.Google Scholar
  4. 4.
    P. Shi, A. Amini, G. Robinson, A. Sinusas, C. T. Constable, and J. Duncan. Shape-based 4D left ventricular myocardial function analysis. In IEEE Workshop on Biomedical Image Analysis, pages 88–97, 1994.Google Scholar
  5. 5.
    D. Rueckert and P. Burger. Shape-based tracking and analysis of the aorta in cardiac MR images using geometrically deformable templates. In Computer Assisted Radiology, pages 274–279, Paris, France, June 1996.Google Scholar
  6. 6.
    T. McInerney and D. Terzopoulos. Deformable models in medical image analysis: A survey. Medical Image Analysis, 1(2), 1996.Google Scholar
  7. 7.
    M. Kass, A. Witkin, and D. Terzopoulos. Snakes — Active contour models. International Journal of Computer Vision, 1(2):259–268, 1987.Google Scholar
  8. 8.
    L. H. Staib and J. S. Ducan. Boundary finding with parametrically deformable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(11):1061–1075, 1992.CrossRefGoogle Scholar
  9. 9.
    G. Székely, A. Kelemen, Ch. Brechbühler, and G. Gerig. Segmentation of 2-D and 3-D objects from MRI volume data using constrained elastic deformations of flexible fourier contour and surface models. Medical Image Analysis, 1(1), 1996.Google Scholar
  10. 10.
    T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham. Active Shape Models — Their Training and Application. Computer Vision and Image Understanding, 61(1):38–59, 1995.CrossRefGoogle Scholar
  11. 11.
    D. Terzopoulos and D. Metaxas. Dynamic 3D models with local and global deformations — deformable superquadrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7):703–714, 1991.CrossRefGoogle Scholar
  12. 12.
    F. L. Bookstein. Applying landmark methods to biological outline data. In K. V. Mardia, C. A. Gill, and I. L. Dryden, editors, Image Fusion and Shape Variability Techniques, pages 59–70, July 1996.Google Scholar
  13. 13.
    G. Wahba. Spline Models for Observational Data. Society for Industrial and Applied Mathematics, 1990.Google Scholar
  14. 14.
    F. L. Bookstein. Principal Warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):567–585, June 1989.CrossRefGoogle Scholar
  15. 15.
    D. Rueckert and P. Burger. Contour fitting using stochastic and probabilistic relaxation for cine MR images. In Computer Assisted Radiology, pages 137–142, Berlin, Germany, June 21–24 1995. Springer-Verlag.Google Scholar
  16. 16.
    S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi. Optimization by Simulated Annealing. Science, 220:671–680, May 1983.Google Scholar
  17. 17.
    J. Besag. On the Statistical Analysis of Dirty Pictures. Journal of the Royal Stat. Soc. B, 48(3):259–302, 1986.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Daniel Rueckert
    • 1
  • Peter Burger
    • 1
  1. 1.Imperial College of Science, Technology and MedicineUSA

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