Skip to main content

Novel Quantitative Method for Spleen’s Morphometry in Splenomegally

  • Conference paper
Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

Included in the following conference series:

Abstract

Novel method for spleen’s semiautomatic accurate quantitative morphometry adaptable in diagnosis of splenomegally is described. The method is based on multiscale wavelet image decomposition, Bayesian inference that reveals the most probable structure delineation in the image and spectral method to smooth and approximate the most probable representation of a real contour hidden in a noisy or fuzzy data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cheong, H.: Double Fourier series on a sphere: Applications to elliptic and vorticity equations. Jour. of Comp. Physics 157(1), 327–349 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  2. Dydenko, I., Friboulet, D., Gorce, J.M., D’hooge, J., Bijnens, B., Magnin, I.E.: Towards ultrasound cardiac image segmentation based on the radiofrequency signal. Medical Image Analysis 7, 353–367 (2003)

    Article  Google Scholar 

  3. Hammoude, A.: Endocardial border identification in two-dimensional echocardiografic images: review of methods. Comp. Med. Imag. Graph. 32, 181–193 (1998)

    Article  Google Scholar 

  4. Heimdal, A., Stoylen, A., Torp, H., Skjaerpe, T.: Real-time strain rate imaging of the left ventricle by ultrasound. J. Am. Soc. Echocard. 11, 1013–1019 (1998)

    Article  Google Scholar 

  5. Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real time algorithm for signal analysis with the help of the wavelet transform, Wavelets: Time-Frequency Methods and Phase-Space, pp. 286–297. Springer, Heidelberg (1989)

    Google Scholar 

  6. Laidlaw, D.H., Fleischer, K.W., Barr, A.H.: Partial volume segmentation with voxel histograms. In: Bankman, I.N. (ed.) Handbook of Medical Imaging, Processing and Analysis, pp. 195–214. Academic Press, London (2000)

    Chapter  Google Scholar 

  7. Li, J., Hero, A.O.: A Fast Spectral Method for Active 3D Shape Reconstruction. Jour. of Math. Imaging and Vision 20, 73–87 (2004)

    Article  MathSciNet  Google Scholar 

  8. Mitchell, S.C., Bosch, J.G., Lelieveldt, B.P.F., van der Geest, R.J., Reiber, J.H.C., Sonka, M.: 3-D Active Appearrance Models: Segmentation of Cardiac MR and Ultrasound Images. IEEE Trans. on Medical Imaging 21(9), 1167–1178 (2002)

    Article  Google Scholar 

  9. Soltysinski, T.: Novel algorithm for active surface reconstruction by Bayesian constrained spectral method. In: Hozman, J., Kneppo, P. (eds.) IFMBE Proceedings, Prague: IFMBE, Proceedings of the 3rd European Medical & Biological Engineering Conference - EMBEC05. Prague, Czech Republic, 20-25.11.2005, vol. 11, pp. 4191–4195 (2005) ISSN 1727-1983

    Google Scholar 

  10. Soltysinski, T.: Bayesian constrained spectral method for segmentation of noisy medical images. Theory and applications. In: Sordo, M., Sachin, V., Jain, L.C. (eds.) Advanced Computational Intelligence Paradigms in Healthcare - 3, Studies in Computational Intelligence, vol. 107, Springer, Heidelberg (2008)

    Google Scholar 

  11. Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41, 233–260 (1988)

    Article  Google Scholar 

  12. Beucher, S., Lantuejoul, C.: Use of watersheds in contour detection. In: International Workshop on image processing, real-time edge and motion detection/estimation, Rennes, France (September 1979)

    Google Scholar 

  13. Kass, M., Witkin, A., Terzopoulos, D.: Snakes - Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1987)

    Article  Google Scholar 

  14. Osher, S., Sethian, J.A.: Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi Formulations. Journal of Computational Physics 79, 12–49 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  15. McInerney, T., Terzopulos, D.: Deformable models in medical image analysis: A survey. Med. Img. Analysis (2) (1996)

    Google Scholar 

  16. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 158–175 (1995)

    Article  Google Scholar 

  17. Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  18. Bernardo, J.M., Smith, A.F.M.: Bayesian Theory. John Wiley & Sons, New York (1994)

    MATH  Google Scholar 

  19. Berry D.A.: Statistics: A Bayesian Perspective, Duxbury, Belmont (1996)

    Google Scholar 

  20. Berger, J.O.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985)

    MATH  Google Scholar 

  21. Box, G.E.P., Tiao, G.C.: Bayesian Inference in Statistical Analysis. John Wiley & Sons, New York (1973)

    MATH  Google Scholar 

  22. de Stefano, A., White, P.R.: Selection of thresholding scheme for image noise reduction on wavelet components using bayesian estimation. Jour. of Math.Imaging and Vision 21, 225–233 (2004)

    Article  Google Scholar 

  23. Sharon, E., Brandt, A., Basri, R.: Segmentation and boundary detection using multiscale intensity measurements. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 469 (2001)

    Google Scholar 

  24. Soltysinski, T., Kałużyński, K., Pałko, T.: Cardiac ventricle contour reconstruction in ultrasonographic images using Bayesian constrained spectral method. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 988–997. Springer, Heidelberg (2006)

    Google Scholar 

  25. Soltysinski, T.: Speckle Noise Removal By ‘A Trous Decomposition And Threshold-limited Synthesis In Ultrasonic Cardiac Images. In: Proceedings of the conference Biosignal 2006, Brno Technical University, Czech Republic, vol. 18 (2006)

    Google Scholar 

  26. Gravel, P., Beaudoin, G., De Guise, J.A.: A method for modeling noise in medical images. IEEE Transactions on medical imaging 23(10), 1221–1232 (2004)

    Article  Google Scholar 

  27. Starck, J.-L., Murtagh, F.: Astronomical image and data analysis. Springer, Berlin (2002)

    Google Scholar 

  28. Soltysinski, T.: Influence of multiscale denoising on Bayesian constrained spectral method in segmentation of noisy medical images. IFMBE Proceedings 14 (2006)

    Google Scholar 

  29. Sołtysiński, T.: Novel Bayesian constrained spectral method for cell morphometry in multimodal molecular imaging. Molecular Imaging 6(5), 366 (2007)

    Google Scholar 

  30. Bankman, I.N. (ed.): Handbook of Medical Imaging, Processing and Analysis. Academic Press, London (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sołtysiński, T. (2008). Novel Quantitative Method for Spleen’s Morphometry in Splenomegally. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_93

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-69731-2_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics