Advertisement

Non-rigid motion models for tracking the left-ventricular wall

  • A A Amini
  • R L Owen
  • P Anandan
  • J S Duncan
6. Anatomical Models And Variability
Part of the Lecture Notes in Computer Science book series (LNCS, volume 511)

Abstract

A unified framework for visual motion tracking of non-rigid objects with specific applications to the left ventricular endocardial wall motion is outlined. The theory considers both two dimensional contours and three dimensional surfaces and in each case uses an elastic model of the object with constraints on the types of motion allowed for tracking the movement. The basic theme in both two and three dimensional analysis is to match bending and stretching properties of shapes in consecutive time instances for deducing quantitative information about the motion of the LV wall. Several algorithms are presented, and applications to real and simulated data are included. At the end, future directions for research are discussed.

Keywords

optical flow computational differential geometry two-dimensional motion three-dimensional motion bending energy conformal stretching 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aggarwal JK (1986). Motion and Time-Varying Imagery — An Overview. Proceedings of the Workshop on Visual Motion. IEEE Computer Society Press, Washington, D.C., pp. 1–6.Google Scholar
  2. Amini AA, Weymouth TE and Jain R. (1990). Using Dynamic Programming for Solving Variational Problems in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-12:855–867.Google Scholar
  3. Amini AA. (1990). Using Dynamic Programming for Solving Variational Problems in Vision: Applications Involving Deformable Models for Contours and Surfaces. PhD Thesis. The University of Michigan, Ann Arbor.Google Scholar
  4. Anandan P. (1989). A Computational Framework and an Algorithm for the Measurement of Visual Motion. International Journal of Computer Vision, Vol-2:283–310.Google Scholar
  5. Areeda J Garcia E Vantrain K Brown D Waxman A and Berman D. (1982). A Comprehensive Method for Automatic Analysis of Rest/Exercise Ventricular Function from Radionuclide Angiography. Digital Imaging: Clinical Advances in Nuclear Medicine. Society of Nuclear Medicine, pp. 241–256.Google Scholar
  6. Axel L and Dougherty L (1989). MR Imaging of Motion with Spatial Modulation of Magnetization. Radiology, Vol-171:841–845.Google Scholar
  7. Ayache N Boissonnat JD Brunet E Cohen L Chieze JP Geiger B Monga O Rocchisani JM and Sander P (1989). Building Highly Structured Volume Representations in 3D Medical Images. Proceedings of Computer-Assisted Radiology, Springer-Verlag, Berlin, pp. 765–772.Google Scholar
  8. Besl P and Jain R (1986). Invariant Surface Characteristics for 3D Object Recognition. Computer Vision, Graphics, and Image Processing, Vol-30:33–80.Google Scholar
  9. Bookstein F (1989). Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11:567–585.Google Scholar
  10. Collins S and Skorton D (1986). Cardiac Imaging and Image Processing. McGraw Hill, New York.Google Scholar
  11. Courant R and Hilbert D (1957). Methods of Mathematical Physics. Interscience, London.Google Scholar
  12. Duncan JS (1987). Knowledge Directed Left Ventricular Boundary Detection in Equilibrium Radionuclide Angiocardiography. IEEE Transactions on Medical Imaging, MI-6:325–336.Google Scholar
  13. Duncan JS Smeulders A Lee F and Zaret B (1988). Measurement of End Diastolic Shape Deformity Using Bending Energy. Computers in Cardiology, pp. 277–280.Google Scholar
  14. Duncan JS Lee F Smeulders A and Zaret B (1991). A Bending Energy Model for Measurement of Cardiac Shape Deformity. Accepted to IEEE Transactions on Medical Imaging.Google Scholar
  15. Eisenhart LP (1955). A treatize on Differential Geometry of Curves and Surfaces. Ginn and Company, Boston.Google Scholar
  16. Garcia EV Van Train K Maddahi J Prigat F (1985). Quantification of Rotational Thallium-201 Myocardial Tomography. Journal of Nuclear Medicine, Vol-26:17–26.Google Scholar
  17. Gelberg H Brundage B Glantz S and Parmley W (1979). Quantitative Left Ventricular Wall Motion Analysis: A Comparison of Area, Chord and Radial Methods. Circulation, Vol-59:991–1000.Google Scholar
  18. Goldgof D Lee H and Huang T (1988). Motion Analysis of Nonrigid Surfaces. Proceedings of IEEE conference on Computer Vision and Pattern Recognition. IEEE Computer Society Press, Washington, D.C., pp. 375–380.Google Scholar
  19. Harris L Clayton P Marshall H and Warner H (1974). A Technique for the Detection of Asynergistic Motion in the Left Ventricle. Computers and Biomedical Research, Vol-7:380–394.Google Scholar
  20. Hildreth EC (1984). The Measurement of Visual Motion. MIT Press. Cambridge, Massachusetts.Google Scholar
  21. Huang T (1990). Modeling, Analysis, and Visualization of Nonrigid Object Motion. International Conference on Pattern Recognition, Atlantic City, N.J., pp. 361–364.Google Scholar
  22. Ingels N Daughters D Stinson E and Alderman E (1975). Measurement of Midwall Myocardial Dynamics in Intact Man by Radiography of Surgically Implanted Markers. Circulation, Vol-52:859–867.Google Scholar
  23. Kreyszig E (1975). Introduction to Differential Geometry and Riemanian Geometry. University of Toronto Press, Toronto, Canada.Google Scholar
  24. Landau L and Lifshitz EM (1986). Theory of Elasticity. Pergamon Press, Oxford, England.Google Scholar
  25. Lanzer P Botvonick E Schiller N (1984). Cardiac Imaging Using Gated Magnetic Resonance. Radiology, Vol-150:121–127.Google Scholar
  26. Links J Douglass K and Wagner H (1980). Patterns of Ventricular Emptying by Fourier Analysis of Gated Blood Pool Studies. Journal of Nuclear Medicine, Vol-21:978–982.Google Scholar
  27. Mailloux GE Bleau A Bertrand M and Petitclerc R (1987). Computer Analysis of Heart Motion from Two Dimensional Echocardiograms. IEEE Transactions on Biomedical Engineering, BME-34:356–364.Google Scholar
  28. Millman R and Parker G (1977). Elements of Differential Geometry. Prentice-Hall. Englewood Cliffs, New Jersey.Google Scholar
  29. Nagel HH (1978). Formation of an Object Concept by analysis of Systematic Time Variation in the Optically Perciptible Environment. Computer Graphics and Image Processing, Vol-7:149–194.Google Scholar
  30. Owen R Staib L Anandan P and Duncan J (1989). Measurement of Left Ventricular Wall Motion from Contour Shape Deformation. In: Information Processing in Medical Imaging. Ortendahl DA and Llacer J (eds.), Wiley-Liss, Inc., New York, pp. 541–556.Google Scholar
  31. Raichlen JS Trivedi SS Herman GT St. John Sutton MG and Reichek N (1986). Dynamic three-dimensional reconstruction of the left ventricle from two-dimensional echocardiograms. Journal of the American College of Cardiology, Vol-8:364–370.Google Scholar
  32. Sagawa K (1973). The Heart as a Pump. In: Engineering Principles in Physiology, Brown II and Gann (eds), Academic Press, New York. pp. 101–126.Google Scholar
  33. Sheehan FH Bolson EL Dodge HT Mathey DG Schofer J and Woo HW (1986). Advantages and Applications of the Centerline Method for Characterizing Regional Ventricular Function. Circulation, Vol-74:293–305.Google Scholar
  34. Slager C Hooghoudt T Serruys P Schuurbiers J Reiber J Meester G Verdouw P and Hugenholtz R (1986). Quantitative Assessment of Regional Left Ventricular Motion Using Endocardial Landmarks. JACC, Vol-7:317–326.Google Scholar
  35. Staib L and Duncan JS (1989). Parametrically Deformable Contour Models. Computer Vision and Pattern Recognition. IEEE Computer Society Press, Washington, D.C. pp. 98–103.Google Scholar
  36. Terzopoulos D (1987). Elastically Deformable Models. ACM Transactions on Computer Graphics, Vol-27:205–214.Google Scholar
  37. Ullman (1979). Interpretation of Visual Motion. MIT Press, Cambridge, MA.Google Scholar
  38. Zerhouni E Parish D Rogers W Yang A and Shapiro P (1988). Tagging of the Human Heart by Multiplanar Selective RF Saturation for the Analysis of Myocardial Contraction. Abstracts of the Annual Meeting of the Society of Magnetic Resonance in Imaging, San Francisco, California, page 10.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • A A Amini
    • 2
    • 1
  • R L Owen
    • 2
  • P Anandan
    • 3
  • J S Duncan
    • 2
    • 1
  1. 1.Department of Diagnostic RadiologyYale UniversityNew Haven
  2. 2.Department of Electrical EngineeringYale UniversityNew Haven
  3. 3.Department of Computer ScienceYale UniversityNew Haven

Personalised recommendations