Introducing Spectral Estimation for Boundary Detection in Echographic Radiofrequency Images

  • Igor Dydenko
  • Denis Friboulet
  • Isabelle E. Magnin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2230)


In echocardiography, the radio-frequency (RF) image is a rich source of information about the investigated tissues. Nevertheless, very few works are dedicated to boundary detection based on the RF image, as opposed to envelope image. In this paper, we investigate the feasibility and limitations of boundary detection in echocardiographic images based on the spectral contents of the RF signal. Using the system approach, we study on models and simulations how the spectral contents can be used for boundary detection. We then introduce an original method of spectral estimation for boundary detection, and several images are analyzed with its mean. It is shown that, under the condition of high acquisition frequency, it is possible to use the spectral contents for boundary detection, and that improvement can be expected with respect to traditional methods. The conclusions may enable development of a robust boundary detection method, based both on the envelope and the spectral contents of the RF signal.


Spectral Parameter Spectral Estimation Power Spectrum Density Boundary Detection Spectral Content 
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.


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  1. 1.
    F. L. Lizzi, E. J. Feleppa, M. Astor and A. Kalisz. statistics of US spectral parameters for prostate and liver examinations. IEEE Trans. Ultras. Ferr. Freq. Contr., 44(4): 935–942, 1997.CrossRefGoogle Scholar
  2. 2.
    P. Chaturvedi and M. F. Insana. Errors in biased estimators for parametric ultrasonic imaging. IEEE Trans. Med. Imag., 17(1): 53–61, 1998.CrossRefGoogle Scholar
  3. 3.
    M. Mulet-Parada and J. A. Noble. 2D+T acoustic boundary detection in echocardiography. Med. Im. Anal., 4: 21–30, 2000.CrossRefGoogle Scholar
  4. 4.
    J.-F. Giovannelli, G. Demoment and A. Herment. A Bayesian Method for Long AR Spectral Estimation: A Comparative Study. IEEE Trans. Ultras. Ferr. Freq. Contr., 43(2): 220–232, 1996.CrossRefGoogle Scholar
  5. 5.
    S. M. Kay. Modern Spectral Estimation: theory and application., Alan V. Oppenheime, Signal processing series, Englewood Cliffs: Prentice Hall, 1988.Google Scholar
  6. 6.
    J.M. Gorce, D. Friboulet, J. D'hooge, B. Bijnens, and I.E. Magnin. Regularized autoregressive models for a spectral estimation scheme dedicated to medical ultrasonic RF images. IEEE Intern. Ultras. Symp., Toronto (Canada), 1461–1464, 1997.Google Scholar
  7. 7.
    P. Charbonnier, L. Blanc-Feraud, G. Aubert, M. Barlaud. Deterministic edgepreserving regularization in computed imaging. IEEE Trans. Im. Proc., 6(2): 298–311, 1997.Google Scholar
  8. 8.
    D. Geman, and G. Reynolds. Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Mach. Intell., 14(3): 367–383, 1992.CrossRefGoogle Scholar
  9. 9.
    M. F. Insana and D. G. Brown. Acoustic scattering theory applied to soft biological tissues. In: Ultrasonic scattering in biological tissues: 75–124, CRC Press, 1993.Google Scholar
  10. 10.
    R. F. Wagner, M. F. Insana and D. G. Brown. Statistical properties of rf and envelope-detected signals with applications to medical ultrasound. J. Opt. Soc. Am., 4(5): 910–922, 1987.CrossRefGoogle Scholar
  11. 11.
    J. Meunier and M. Bertrand. Echographic image mean gray level changes with tissue dynamics: a system-based model study. IEEE Trans. Biomed. Eng., 42(4): 403–410, 1995.CrossRefGoogle Scholar
  12. 12.
    G. Kitagawa, and W. Guersh. A smoothness priors long ar model method for spectral estimation, IEEE Trans. Autom. Control, 30(1): 57–65, 1985.zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Igor Dydenko
    • 1
  • Denis Friboulet
    • 1
  • Isabelle E. Magnin
    • 1
  1. 1.CREATISINSA - Blaise PascalVilleurbanne Cedex

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