Robust non-homomorphic approach for speckle reduction in medical ultrasound images

Article

Abstract

Most existing wavelet-based image denoising techniques are developed for additive white Gaussian noise. In applications to speckle reduction in medical ultrasound (US) images, the traditional approach is first to perform the logarithmic transform (homomorphic processing) to convert the multiplicative speckle noise model to an additive one, and then the wavelet filtering is performed on the log-transformed image, followed by an exponential operation. However, this non-linear operation leads to biased estimation of the signal and increases the computational complexity of the filtering method. To overcome these drawbacks, an efficient, non-homomorphic technique for speckle reduction in medical US images is proposed. The method relies on the true characterisation of the marginal statistics of the signal and speckle wavelet coefficients. The speckle component was modelled using the generalised Nakagami distribution, which is versatile enough to model the speckle statistics under various scattering conditions of interest in medical US images. By combining this speckle model with the generalised Gaussian signal first, the Bayesian shrinkage functions were derived using the maximum a posteriori (MAP) criterion. The resulting Bayesian processor used the local image statistics to achieve soft-adaptation from homogeneous to highly heterogeneous areas. Finally, the results showed that the proposed method, named GNDShrink, yielded a signal-to-noise ratio (SNR) gain of 0.42 dB over the best state-of-the-art despeckling method reported in the literature, 1.73 dB over the Lee filter and 1.31 dB over the Kaun filter at an input SNR of 12.0 dB, when tested on a US image. Further, the visual comparison of despeckled US images indicated that the new method suppressed the speckle noise well, while preserving the texture and organ surfaces.

Keywords

Ultrasound Redundant discrete wavelet transform Generalised Nakagami distribution Generalised Gaussian distribution MAP estimator Speckle suppression 

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References

  1. Achim, A., Bezerianos, A., andTsakalides, P. (2001): ‘Novel Bayesian multiscale method for speckle removal in medical ultrasound images’,IEEE Trans. Med. Imag.,20, pp. 772–783Google Scholar
  2. Chang, G., Yu, B., andVetterli, M. (2000): ‘Adaptive wavelet thresholding for image denoising and compression’,IEEE Trans. Image Process.,9, pp. 1532–1546MathSciNetGoogle Scholar
  3. Dai, M., Peng, C., Chan, A. K., andLoguinov, D. (2004): ‘Bayesian wavelet shrinkage with edge detection for SAR image despeckling’,IEEE Trans. Geosci. Remote Sens.,42, pp. 1642–1648Google Scholar
  4. Donoho, D. L. (1995): ‘De-noising by soft-thresholding’,IEEE Trans. Inform. Theory,41, pp. 613–627.CrossRefMATHMathSciNetGoogle Scholar
  5. Foucher, S., Bénié, G. B., andBoucher, J. (2001): ‘Multiscale MAP filtering of SAR images’,IEEE Trans. Image Process.,10, pp. 49–60CrossRefGoogle Scholar
  6. Gupta, S., Chauhan, R. C., andSaxena, S. C. (2004): ‘A wavelet based statistical approach for speckle reduction in medical ultrasound images’,Med. Biol. Eng. Comput.,42, pp. 189–192CrossRefGoogle Scholar
  7. Kuan, D.T., Sawchuk, A. A., Strand, T. C., andChavel, P. (1985): ‘Adaptive noise smoothing filter for images with signal dependent noise’,IEEE Trans. Pattern Analy. Mach. Intell.,PAMI-7. pp. 165–177Google Scholar
  8. Lang, M., Guo, H., Odegard, J. E., Burrus, C. S., andWells Jr, R. O. (1996): ‘Noise reduction using an undecimated discrete wavelet transform’,IEEE Signal Process. Lett.,3, pp. 10–12CrossRefGoogle Scholar
  9. Lee, J. S. (1981): ‘Speckle analysis and smoothing of synthetic aperture radar images’,Comput. Graph. Image Process.,17, pp. 24–32Google Scholar
  10. Mallat, S. (1989): ‘A theory for multiresolution signal decomposition: The wavelet representation’,IEEE Trans. Patterns Anal. Mach. Intell.,11, pp. 674–692MATHGoogle Scholar
  11. Mihçak, M. K., Kozintsev, I., Ramchandran, K., andMoulin, P. (1999): ‘Low-complexity image denoising based on statistical modeling of wavelet coefficients’,IEEE Signal Process. Lett.,6, pp. 300–303Google Scholar
  12. Pižurica, A., Philips, W., Lemahieu, I., andAcheroy, M. (2003): ‘A versatile wavelet domain noise filtration technique for medical imaging’,IEEE Trans. Med. Imaging,22, pp. 323–331Google Scholar
  13. Shankar, P. M. (1995): ‘A model for ultrasonic scattering from tissues based on K-distribution’,Phys. Med. Biol.,40, pp. 1633–1649CrossRefGoogle Scholar
  14. Shankar, P. M. (2001): ‘Ultrasonic tissue characterization using a generalized Nakagami model’,IEEE Trans. Ultrason., Ferroelect. Freq. Control,48, pp. 1716–1720Google Scholar
  15. Simard, M., Degrandi, G., Thomson, K. P. B., andBenie, G. B. (1998): ‘Analysis of speckle noise contribution on wavelet decompositions of SAR images’,IEEE Trans. Geosci. Remote Sens.,36, pp. 1953–1962Google Scholar
  16. Solbø, S., andEltoft, T. (2004): ‘Homomorphic wavelet-based statistical despeckling of SAR images’,IEEE Trans. Geosci. Remote Sensing,42, pp. 711–721Google Scholar
  17. Wagner, R. F., Insana, M. F., andBrown, D. G. (1987): ‘Statistical properties of radio-frequency and envelope detected signals with application to medical ultrasound’,J. Opt. Soc. Am.,19, pp. 225–229Google Scholar
  18. Wells, P. N. T., andHalliwell, M. (1981): ‘Speckle in ultrasonic imaging’,Ultrasonic,19, pp. 225–290Google Scholar
  19. Xie, H., Pierce, L., andUlaby, F. T. (2002): ‘Despeckling SAR images using a low-complexity wavelet denoising process’,Proc. IEEE, pp. 321–324Google Scholar
  20. Zong, X., Laine, A. F., andGeiser, E. A. (1998): ‘Speckle reduction and contrast enhancement of echocardiogram via multiscale nonlinear processing’,IEEE Trans. Med. Imag.,17, pp. 532–540Google Scholar

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© IFMBE 2005

Authors and Affiliations

  1. 1.Sant Longowal Institute of Engineering & TechnologyLongowalIndia
  2. 2.Thapar Institute of Engineering & TechnologyPatialaIndia

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