Advertisement

Fast Segmentation of the Mitral Valve Leaflet in Echocardiography

  • Sébastien Martin
  • Vincent Daanen
  • Olivier Chavanon
  • Jocelyne Troccaz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4241)

Abstract

This paper presents a semi-automatic method for tracking the mitral valve leaflet in transesophageal echocardiography. The algorithm requires a manual initialization and then segments an image sequence. The use of two constrained active contours and curve fitting techniques results in a fast segmentation algorithm. The active contours successfully track the inner cardiac muscle and the mitral valve leaflet axis. Three sequences have been processed and the generated muscle outline and leaflet axis have been visually assessed by an expert. This work is a part of a more general project which aims at providing real-time detection of the mitral valve leaflet in transesophageal echocardiography images.

Keywords

Medical Image Analysis Tracking and Motion Active Contours Ultrasound Imaging 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cootes, T., Taylor, C.: Active shape models - smart snakes. In: Proceedings of the British Machine Vision Conference, pp. 266–275 (1992)Google Scholar
  2. 2.
    Chalana, V., Linker, D.T., Haynor, D.R., Kim, Y.: A multiple active contour model for cardiac boundary detection on echocardiographic sequences. IEEE Trans. Med. Imag. 15, 290–298 (1996)CrossRefGoogle Scholar
  3. 3.
    Mignotte, M., Meunier, J., Tardif, J.-C.: Endocardial boundary estimation and tracking in echocardiographic images using deformable templates and markov random fields. Pattern Analysis and Applications 4, 256–271 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Mailloux, G.E., Langlois, F., Simard, P.Y., Bertrand, M.: Restoration of the velocity field of the heart from two-dimensional echocardiograms. IEEE Trans. Med. Imag. 8, 143–153 (1989)CrossRefGoogle Scholar
  5. 5.
    Adam, D., Hareuveni, O., Sideman, S.: Semiautomated border tracking of cine echocardiographic ventricular images field of the heart from two-dimensional echocardiograms. IEEE Trans. Med. Imag. 6, 266–271 (1987)CrossRefGoogle Scholar
  6. 6.
    Jacob, G., Noble, J., Behrenbruch, C., Kelion, A., Banning, A.: A shape-space-based approach to tracking myocardial borders and quantifying regional left-ventricular function applied in echocardiography. IEEE Trans. Med. Imag. 21, 226–238 (2002)CrossRefGoogle Scholar
  7. 7.
    Chen, A.A.a., Elayyadi, M., Radeva, P.: Tag surface reconstruction and tracking of myocardial beads from spamm-mri with parametric b-spline surfaces. IEEE Trans. Med. Imag. 20, 94–103 (2001)CrossRefGoogle Scholar
  8. 8.
    Lorenzo-Valdés, M., Sanchez-Ortiz, G.I., Mohiaddin, R.H., Rueckert, D.: Atlas-based segmentation of 4d cardiac mr sequences using nonrigid registration (2002)Google Scholar
  9. 9.
    Lelieveldt, B., Mitchell, S.C., Bosch, J.G., van der Geest, R.J., Sonka, M., Reiber, J.H.C.: Time-Continuous Segmentation of Cardiac Image Sequences Using Active Appearance Motion Models. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 249–256. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Montillo, A., Metaxas, D., Axel, L.: Automated segmentation of the left and right ventricles in 4D cardiac SPAMM images. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, p. 620. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Comaniciu, D., Zhou, X.S., Chen, S.A.Y., Elayyadi, M., Radeva, P.: Robust Real-Time Myocardial Border Tracking for Echocardiography: An Information Fusion Approach. IEEE Trans. Med. Imag. 20, 94–103 (2001)CrossRefGoogle Scholar
  12. 12.
    Mikic, I., Krucinski, S., Thomas, J.D.: Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates. IEEE Trans. Med. Imag. 17, 274–284 (1998)CrossRefGoogle Scholar
  13. 13.
    Jansen, C., Arigovindan, M., Sühling, M., Marsch, S., Unser, M., Hunziker, P.: Multidimensional, multistage wavelet footprints: A new tool for image segmentation and feature extraction in medical ultrasound. In: Sonka, M., Fitzpatrick, J. (eds).: Progress in Biomedical Optics and Imaging, vol. 4(23), 5032: Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing (MI 2003) (Part II), San Diego CA, USA (2003) 762–767Google Scholar
  14. 14.
    Blake, A., Isard, M.: Active Contour. Springer, Heidelberg (1998)Google Scholar
  15. 15.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  16. 16.
    Amini, A.A., Weymouth, T.E., Jain, R.C.: Using dynamic programming for solving variational problems in vision. IEEE Trans. Pattern Anal. Mach. Intell. 12, 855–867 (1990)CrossRefGoogle Scholar
  17. 17.
    Lachaud, J.-O., Taton, B.: Deformable model with a complexity independent from image resolution. Computer Vision and Image Understanding (accepted to appear, 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sébastien Martin
    • 1
  • Vincent Daanen
    • 1
  • Olivier Chavanon
    • 1
    • 2
  • Jocelyne Troccaz
    • 1
  1. 1.Faculté de Médecine –TIMC Lab, CAMI Team, Institut d’Ingénierie d’Information de Santé (IN3S)La TroncheFrance
  2. 2.Cardiac Surgery DepartmentGrenoble University HospitalGrenobleFrance

Personalised recommendations