[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Wavelet-based statistical approach for speckle reduction in medical ultrasound images

  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

A novel speckle-reduction method is introduced, based on soft thresholding of the wavelet coefficients of a logarithmically transformed medical ultrasound image. The method is based on the generalised Gaussian distributed (GGD) modelling of sub-band coefficients. The method used was a variant of the recently published BayesShrink method by Chang and Vetterli, derived in the Bayesian framework for denoising natural images. It was scale adaptive, because the parameters required for estimating the threshold depend on scale and sub-band data. The threshold was computed by Kσ/σx, where σ and σx were the standard deviation of the noise and the sub-band data of the noise-free image, respectively, and K was a scale parameter. Experimental results showed that the proposed method outperformed the median filter and the homomorphic Wiener filter by 29% in terms of the coefficient of correlation and 4% in terms of the edge preservation parameter. The numerical values of these quantitative parameters indicated the good feature preservation performance of the algorithm, as desired for better diagnosis in medical image processing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Achim, A., Bezerianos, A., andTsakalides, P. (2001) ‘Novel Bayesian multiscale method for speckle removal in medical ultrasound images’,IEEE Trans. Med. Imag.,20, pp. 772–783

    Google Scholar 

  • Arsenault, H. H., andApril, G. (1976): ‘Properties of speckle integrated with a finite aperture and logarithmically transformed’,J. Opt. Soc. Am.,66, 1160–1163

    Google Scholar 

  • Chang, G., Yu B., andVetterli, M. (2000): ‘Spatially adaptive wavelet thresholding with context modelling for image denoising’,IEEE Trans. Image. Proc.,9, pp. 1522–1531

    MathSciNet  Google Scholar 

  • Chivers, R. C., Davies, I. J., andDuarte, F. M. (1986): ‘Perceptual studies and ultrasonic B-scam textures’,Phys. Med. Biol.,31, pp. 627–634

    Google Scholar 

  • Donoho, D. L., andJohnstone, I. M. (1994): ‘Ideal spatial adaptation via wavelet shrinkage’,Biometrica,81, pp. 425–455

    MathSciNet  Google Scholar 

  • Donolho, D. L. (1995): ‘Denoising by soft-thresholding’,IEEE Trans. Inf. Theory,41, pp. 613–627

    Google Scholar 

  • Donolho, D. L., andJohnstone, I. M. (1995): ‘Adapting to unknown smoothness via wavelet shrinkage’,J. Am. Stat. Assoc.,90, pp. 1200–1224

    Google Scholar 

  • Ghofrani, S., Jahed-Motlagh, M. R., andAyotollahi, A. (2001): ‘An adaptive speckle suppression filter based on Nakagami distribution’ (IEEE, 2001)

  • Goodman, J. W. (1976): ‘Some fundamental properties of Speckle’,J. Opt. Soc. Am.,66, pp. 1145–1150

    Google Scholar 

  • Guo, H., Odegard, M. L., Gopinath, R. A., Selesnick, I. W., andBurrus, C. S. (1994): ‘Wavelet based speckle reduction with application to SAR based ATD/R’,Proc. ICIP, Vol.1, pp. 75–79

    Google Scholar 

  • Gupta, S., andKaur, L. (2002): ‘Wavelet based image compression using Daubechies filters’.8th Nat. Conf. on Communications, IIT, Bombay

  • Jain, A. K. (1989): ‘Fundamentals of digital image processing’ (Prentice Hall, Englewood Cliffs, NJ, 1989)

    Google Scholar 

  • Jansene, M. (2001): ‘Noise reduction using wavelets, Lecture series in Statistics’, (Springer-Verlag, New York, 2001)

    Google Scholar 

  • Joshi, R. L., Crump, V. J., andFisher, T. R. (1995): ‘Image subband coding using arithmetic and trellis coded quantization’,IEEE Trans. Circuits Syst. Video Technol.,5, pp. 515–523

    Article  Google Scholar 

  • Kremkau, F. W., andTaylor, K. J. W. (1986): ‘Artefacts in ultrasound imaging’,J. Ultrasound Med.,5, pp. 227–237

    Google Scholar 

  • Loannidis, A., Kazakos, D., andWatson, D. D. (1984): ‘Application of median filtering on nuclear medicine scintigram images’,Proc. 7th Int. Conf. Pattern Recognition, pp. 33–36

  • Loupas, T., McDicken, W. N., andAllen, P. L. (1987): ‘Noise reduction in ultrasonic images by digital filtering’,Br. J. Radiol.,60, pp. 389–392

    Google Scholar 

  • Lowe, H., Bamber, J. C., Webb, S., andCook-Martin, G. (1988): ‘Perceptual studies of contrast, texture and detail in ultrasound B-scans’,SPIE Proc.,914, pp. 40–47

    Google Scholar 

  • Mallat, S. (1989): ‘A theory for multiresolution signal decomposition: the wavelet representation’,IEEE Trans. Patterns Anal. Mach. Intell.,11, pp. 674–692

    MATH  Google Scholar 

  • Mallat, S., (1998): ‘A wavelet tour of signal processing’ (Academic Press, New York, 1998)

    Google Scholar 

  • Ritenour, E. R., Nelson, T. R., andRaff, U. (1984): ‘Application of median filter to digital readiographic images’.Proc. 7th Int. Conf. Acoust. Speech, Signal Processing, pp. 23.1.1–23.1.4

  • Sattar, F., Floreby, L., Salomonsson, G., andLovstrom, B. (1997): ‘Image enhancement based on a nonlinear multiscale method’,IEEE Trans. Image Proc.,6, pp. 888–895

    Google Scholar 

  • Simoncelli, E. P., andAdelson, E. H. (1996): ‘Noise removal via Bayesian wavelet coring’,Third Int. Conf. Image Processing,1, pp. 379–382

    Google Scholar 

  • Smith, S. W., andLopez, H. (1982): ‘A contrast detail analysis of diagnostic ultrasound imaging’,Med. Phys.,9, pp. 4–12

    Article  Google Scholar 

  • Stein, C. M. (1981): ‘Estimation of the mean of a multivariate normal distribution’,Ann. Statist.,9, pp. 1135–1151

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Sant Longowal Institute of Engineering & Technology, Longowal, PB, India

    S. Gupta & R. C. Chauhan

  2. Thapar Institute of Engineering & Technology, Patiala, India

    S. C. Sexana

Authors
  1. S. Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. C. Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. C. Sexana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Gupta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gupta, S., Chauhan, R.C. & Sexana, S.C. Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Med. Biol. Eng. Comput. 42, 189–192 (2004). https://doi.org/10.1007/BF02344630

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02344630

Keywords

Search

Navigation