Displacement Calculation of Heart Walls in ECG Sequences Using Level Set Segmentation and B-Spline Free Form Deformations

  • Andrzej Skalski
  • Paweł Turcza
  • Tomasz Zieliński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6375)


In the paper a problem of displacement calculation of the walls of left heart ventricle in echocardiographic (ECG) ultrasound sequences/videos is addressed. A novel method, which is proposed in it, consists of: 1) speckle reduction anisotrophic diffusion (SRAD) filtration of ultrasonography (USG) images, 2) segmentation of heart structures in consecutive de-noised frames via active contour without edges method, 3) calculation of left ventricle frame-to-frame deformation vectors by B-Spline Free Form Deformation (FFD) algorithm. Results from method testing on synthetic USG-like and real ECG images are presented in the paper.


Active Contour Active Contour Model Speckle Noise Heart Wall Left Heart Ventricle 
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.
    Bharali, U., Ghosh, D.: Cardiac Motion Estimation from Echocardiographic Image Sequence using Unsupervised Active Contour Tracker. In: Proc. ICARCV (2006)Google Scholar
  2. 2.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Process. 10(2), 266–277 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    D’hooge, J., et al.: Regional Strain and Strain Rate Measurements by Cardiac Ultrasound: Principles, Implementation and Limitations. Eur. J. Echocardiogr. 1, 154–170 (2000)CrossRefGoogle Scholar
  4. 4.
    Dydenko, I., et al.: A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography. Med. Image Anal. 10, 162–177 (2006)CrossRefGoogle Scholar
  5. 5.
    Aja-Fernández, S., Lopez, C.A.: On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image Process. 15(9), 2694–2701 (2006)CrossRefGoogle Scholar
  6. 6.
    Gilliam, A.D., Acton, S.T.: Echocardiographic Simulation for Validation of Automated Segmentation Methods. In: IEEE ICIP 2007, pp. 529–532 (2007)Google Scholar
  7. 7.
    Jacob, G., et al.: Shape-Space-Based Approach to Tracking Myocardial Borders and Quantifying Regional Left-Ventricular Function Applied in Echocardiography. IEEE Trans. Med. Imag. 21(3), 226–238 (2002)CrossRefGoogle Scholar
  8. 8.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Comp. Vis. 1, 321–331 (1988)CrossRefGoogle Scholar
  9. 9.
    Kuan, D.T., Sawchuk, A.A., Strand, T.C.: Adaptive noise smoothing filter with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell., PAMI 7(2), 165–177 (1985)CrossRefGoogle Scholar
  10. 10.
    Malassiotis, S., Strintzis, M.G.: Tracking the Left Ventricle in Echocardiographic Images by Learning Heart Dynamics. IEEE Trans. Med. Imag. 18(3) (1999)Google Scholar
  11. 11.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. J. Comp. Phys. 79, 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 12(7), 629–639 (1990)CrossRefGoogle Scholar
  13. 13.
    Perreault, C., Auclair-Fortier, M.-F.: Speckle Simulation Based on B-Mode Echographic Image Acquisition Model. In: 4th Canadian Conf. CRV 2007, pp. 379–386 (2007)Google Scholar
  14. 14.
    Rueckert, D., et al.: Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)CrossRefGoogle Scholar
  15. 15.
    Skalski, A., et al.: Left Ventricle USG image segmentation using Active Contour Model. In: Int. Conf. on Comput. Sci., Amsterdam (2010) (accepted)Google Scholar
  16. 16.
    Yu, Y., Acton, S.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11, 1260–1270 (2002)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andrzej Skalski
    • 1
  • Paweł Turcza
    • 1
  • Tomasz Zieliński
    • 2
  1. 1.Department of Measurement and InstrumentationAGH University of Science and TechnologyKrakówPoland
  2. 2.Department of Telecommunications AGH University of Science and TechnologyKrakówPoland

Personalised recommendations