Tracking of LV Endocardial Surface on Real-Time Three-Dimensional Ultrasound with Optical Flow

  • Qi Duan
  • Elsa D. Angelini
  • Susan L. Herz
  • Olivier Gerard
  • Pascal Allain
  • Christopher M. Ingrassia
  • Kevin D. Costa
  • Jeffrey W. Holmes
  • Shunichi Homma
  • Andrew F. Laine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3504)


Matrix-phased array transducers for real-time three-dimensional ultrasound enable fast, non-invasive visualization of cardiac ventricles. Segmentation of 3D ultrasound is typically performed at end diastole and end systole with challenges for automation of the process and propagation of segmentation in time. In this context, given the position of the endocardial surface at certain instants in the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of volume ultrasound data. In this paper, we applied optical flow to track the endocardial surface between frames of reference, segmented via manual tracing or manual editing of the output from a deformable model. To evaluate optical-flow tracking of the endocardium, quantitative comparison of ventricular geometry and dynamic cardiac function are reported on two open-chest dog data sets and a clinical data set. Results showed excellent agreement between optical flow tracking and segmented surfaces at reference frames, suggesting that optical flow can provide dynamic “interpolation” of a segmented endocardial surface.


Root Mean Square Error Optical Flow Deformable Model Endocardial Surface Ventricular Geometry 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ofili, E.O., Nanda, N.C.: Three-Dimensional and Four-Dimensional Echocardiography. Ultrasound Medical Biology 20 (1994)Google Scholar
  2. 2.
    Fenster, A., Downey, D.B.: Three-Dimensional Ultrasound Imaging. In: Handbook of Medical Imaging. Physics and Psychophysics, vol. 1, pp. 463–510 (2000)Google Scholar
  3. 3.
    Rankin, R.N., Fenster, A., et al.: Three-Dimensional Sonographic Reconstruction: Technique and Diagnostic Applications. American Journal of Radiology 161, 695–702 (1993)Google Scholar
  4. 4.
    Belohlavek, M., Foley, D.A., et al.: Three- and Four-Dimensional Cardiovascular Ultrasound Imaging: A New Era for Echocardiography. In: Mayo Clinic Proceedings, vol. 68, pp. 221–240 (1993)Google Scholar
  5. 5.
    Ramm, O.T.V., Smith, S.W.: Real Time Volumetric Ultrasound Imaging System. Journal of Digital Imaging 3, 261–266 (1990)CrossRefGoogle Scholar
  6. 6.
    Herz, S., Pulerwitz, T., et al.: Novel Technique for Quantitative Wall Motion Analysis Using Real-Time Three-Dimensional Echocardiography. In: Proceedings of the 15th Annual Scientific Sessions of the American Society of Echocardiography (2004)Google Scholar
  7. 7.
    Philips Ultrasound - Ie33 (2004)Google Scholar
  8. 8.
    Tsuruoka, S., Umehara, M., et al.: Regional Wall Motion Tracking System for High-Frame Rate Ultrasound Echocardiography. In: Proceedings of the 1996 4th International Workshop on Advanced Motion Control, AMC 1996. Part 1, Tsu, Jpn (1996)Google Scholar
  9. 9.
    Mikic, I., Krucinski, S., et al.: Segmentation and Tracking in Echocardiographic Sequences: Active Contours Guided by Optical Flow Estimates. IEEE Trans. Med. Imaging 17, 274–284 (1998)CrossRefGoogle Scholar
  10. 10.
    Boukerroui, D., Noble, J.A., et al.: Estimation in Ultrasound Images: A Block Matching Approach. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 586–598. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Yu, W., Lin, N., et al.: Motion Analysis of 3d Ultrasound Texture Patterns. In: Magnin, I.E., Montagnat, J., Clarysse, P., Nenonen, J., Katila, T. (eds.) FIMH 2003. LNCS, vol. 2674, pp. 252–261. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Paragios, N.: A Level Set Approach for Shape-Driven Segmentation and Tracking of the Left Ventricle. IEEE Trans. Med. Imaging 22, 773–776 (2003)CrossRefGoogle Scholar
  13. 13.
    Bardinet, E., Cohen, L.D., et al.: Tracking and Motion Analysis of the Left Ventricle with Deformable Superquadratics. Med. Image Analysis 1, 129–149 (1996)CrossRefGoogle Scholar
  14. 14.
    Behar, V., Adam, D., et al.: The Combined Effect of Nonlinear Filtration and Window Size on the Accuracy of Tissue Displacement Estimation Using Detected Echo Signals. Ultrasonics 41, 743–753 (2004)CrossRefGoogle Scholar
  15. 15.
    Bang, J., Dahl, T., et al.: A New Method for Analysis of Motion of Carotid Plaques from Rf Ultrasound Images. Ultrasound Med. Biol. 29, 967–976 (2003)CrossRefGoogle Scholar
  16. 16.
    Rabben, S.I., Bjaerum, S., et al.: Ultrasound-Based Vessel Wall Tracking: An Auto-Correlation Technique with Rf Center Frequency Estimation. Ultrasound Med. Biol. 28, 507–517 (2002)CrossRefGoogle Scholar
  17. 17.
    D’Hooge, J., Claus, P., et al.: Deformation Imaging by Ultrasound for the Assessment of Regional Myocardial Function. In: 2003 IEEE Ultrasonics Symposium, Honolulu, HI, USA (2003)Google Scholar
  18. 18.
    Konofagou, E.E., Manning, W., et al.: Myocardial Elastography - Comparison to Results Using Mr Cardiac Tagging. In: 2003 IEEE Ultrasonics Symposium, Honolulu, HI, United States (2003)Google Scholar
  19. 19.
    Gutierrez, M.A., Moura, L., et al.: Computing Optical Flow in Cardiac Images for 3d Motion Analysis. In: Proceedings of the 1993 Conference on Computers in Cardiology, London, UK (1993)Google Scholar
  20. 20.
    Shin, I.-S., Kelly, P.A., et al.: Left Ventricular Volume Estimation from Three-Dimensional Echocardiography. In: Proceedings of SPIE, Medical Imaging 2004 - Ultrasonic Imaging and Signal Processing, San Diego, CA, United States (2004)Google Scholar
  21. 21.
    Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: International Joint Conference on Artificial Intelligence, IJCAI (1981)Google Scholar
  22. 22.
    Horn, B.K.P., Schunck, B.G.: Determining Optical Flow. Artificial Intelligence 17 (1981)Google Scholar
  23. 23.
    Nagel, H.: Displacement Vectors Derived from Second-Order Intensity Variations in Image Sequences. Computer Vision Graphics Image Processing 21, 85–117 (1983)CrossRefGoogle Scholar
  24. 24.
    Anandan, P.: A Computational Framework and an Algorithm for the Measurement of Visual Motion. International Journal of Computer Vision 2, 283–310 (1989)CrossRefGoogle Scholar
  25. 25.
    Singh, A.: An Estimation-Theoretic Framework for Image-Flow Computation. In: International Conference on Computer Vision (1990)Google Scholar
  26. 26.
    Barron, J.L., Fleet, D., et al.: Performance of Optical Flow Techniques. Int. J. of Computer Vision 12, 43–77 (1994)CrossRefGoogle Scholar
  27. 27.
    Duan, Q., Angelini, E.D., et al.: Assessment of Visual Quality and Spatial Accuracy of Fast Anisotropic Diffusion and Scan Conversion Algorithms for Real-Time Three-Dimensional Spherical Ultrasound. In: SPIE International Symposium Medical Imaging, San Diego, CA, USA (2004)Google Scholar
  28. 28.
    Ingrassia, C.M., Herz, S.L., et al.: Impact of Ischemic Region Size on Regional Wall Motion. In: Proceedings of the 2003 Annual Fall Meeting of the Biomedical Engineering Society (2003)Google Scholar
  29. 29.
    Angelini, E.D., Hamming, D., et al.: Comparison of Segmentation Methods for Analysis of Endocardial Wall Motion with Real-Time Three-Dimensional Ultrasound. In: Computers in Cardiology, Memphis TN, USA (2002)Google Scholar
  30. 30.
    Herz, S., Pulerwitz, T., et al.: Novel Technique for Quantitative Wall Motion Analysis Using Real-Time Three-Dimensional Echocardiography. In: Annual Scientific Sessions of the American Society of Echocardiography (2004)Google Scholar
  31. 31.
    Schiller, N.B., Acquatella, H., et al.: Left Ventricular Volume from Paired Biplane Two-Dimensional Echocardiography. Circulation 60, 547–555 (1979)Google Scholar
  32. 32.
    Folland, E.D., Parisi, A.F., et al.: Assessment of Left Ventricular Ejection Fraction and Volumes by Real-Time, Two-Dimensional Echocardiography. A Comparison of Cineangiographic and Radionuclide Techniques. Circulation 60, 760–766 (1979)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qi Duan
    • 1
  • Elsa D. Angelini
    • 2
  • Susan L. Herz
    • 1
  • Olivier Gerard
    • 3
  • Pascal Allain
    • 3
  • Christopher M. Ingrassia
    • 1
  • Kevin D. Costa
    • 1
  • Jeffrey W. Holmes
    • 1
  • Shunichi Homma
    • 1
  • Andrew F. Laine
    • 1
  1. 1.Columbia UniversityNew YorkUSA
  2. 2.Ecole Nationale Supérieure des TélécommunicationsParisFrance
  3. 3.Philips Medical Systems Research ParisSuresnesFrance

Personalised recommendations