Real-Time Tracking of the Left Ventricle in 3D Echocardiography Using a State Estimation Approach

  • Fredrik Orderud
  • Jøger Hansgård
  • Stein I. Rabben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)


In this paper we present a framework for real-time tracking of deformable contours in volumetric datasets. The framework supports composite deformation models, controlled by parameters for contour shape in addition to global pose. Tracking is performed in a sequential state estimation fashion, using an extended Kalman filter, with measurement processing in information space to effectively predict and update contour deformations in real-time. A deformable B-spline surface coupled with a global pose transform is used to model shape changes of the left ventricle of the heart.

Successful tracking of global motion and local shape changes without user intervention is demonstrated on a dataset consisting of 21 3D echocardiography recordings. Real-time tracking using the proposed approach requires a modest CPU load of 13% on a modern computer. The segmented volumes compare to a semi-automatic segmentation tool with 95% limits of agreement in the interval 4.1 ±24.6 ml (r = 0.92).


Contour Point Active Shape Model Segmented Contour Segmented Volume Deformable Contour 
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.


  1. 1.
    Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. Medical Imaging, IEEE Transactions on 25(8), 987–1010 (2006)CrossRefGoogle Scholar
  2. 2.
    Orderud, F.: A framework for real-time left ventricular tracking in 3D+T echocardiography, using nonlinear deformable contours and kalman filter based tracking. In: Computers in Cardiology (2006)Google Scholar
  3. 3.
    Blake, A., Curwen, R., Zisserman, A.: A framework for spatiotemporal control in the tracking of visual contours. International Journal of Computer Vision 11(2), 127–145 (1993)CrossRefGoogle Scholar
  4. 4.
    Blake, A., Isard, M.: Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion. Springer-Verlag, New York, Inc. Secaucus, NJ, USA (1998)Google Scholar
  5. 5.
    Jacob, G., Noble, J.A., Mulet-Parada, M., Blake, A.: Evaluating a robust contour tracker on echocardiographic sequences. Medical Image Analysis 3(1), 63–75 (1999)CrossRefGoogle Scholar
  6. 6.
    Jacob, G., Noble, J.A., Kelion, A.D., Banning, A.P.: Quantitative regional analysis of myocardial wall motion. Ultrasound in Medicine & Biology 27(6), 773–784 (2001)CrossRefGoogle Scholar
  7. 7.
    Jacob, G., Noble, J.A., Behrenbruch, C., Kelion, A.D., Banning, A.P.: A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography. Medical Imaging, IEEE Transactions on 21(3), 226–238 (2002)CrossRefGoogle Scholar
  8. 8.
    Park, J., Metaxas, D., Young, A.A., Axel, L.: Deformable models with parameter functions for cardiac motion analysis from tagged MRI data. Medical Imaging, IEEE Transactions on 15(3), 278–289 (1996)CrossRefGoogle Scholar
  9. 9.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - Their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  10. 10.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. Medical Imaging, IEEE Transactions on 18(8), 712–721 (1999)CrossRefGoogle Scholar
  11. 11.
    Comaniciu, D., Zhou, X.S., Krishnan, S.: Robust real-time myocardial border tracking for echocardiography: An information fusion approach. Medical Imaging, IEEE Transactions on 23(7), 849–860 (2004)CrossRefGoogle Scholar
  12. 12.
    Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation. Wiley-Interscience, Chichester (2001)Google Scholar
  13. 13.
    Rabben, S.I., Torp, A.H., Støylen, A., Slørdahl, S., Bjørnstad, K., Haugen, B.O., Angelsen, B.: Semiautomatic contour detection in ultrasound M-mode images. Ultrasound in Med. & Biol. 26(2), 287–296 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fredrik Orderud
    • 1
  • Jøger Hansgård
    • 2
  • Stein I. Rabben
    • 3
  1. 1.Norwegian University of Science and TechnologyNorway
  2. 2.University of OsloNorway
  3. 3.GE Vingmed UltrasoundNorway

Personalised recommendations