Comparing Nonsubsampled Wavelet, Contourlet and Shearlet Transforms for Ultrasound Image Despeckling

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Sedigheh Ghofrani 1,*

1. Electrical and Electronic Engineering Department, Islamic Azad University, Tehran South Branch, Tehran, Postal Code 15317-64611, Iran

* Corresponding author.


Received: 19 Sep. 2014 / Revised: 25 Oct. 2014 / Accepted: 3 Dec. 2014 / Published: 8 Jan. 2015

Index Terms

Nonsubsampled Wavelet, nonsubsampled Contourlet, nonsubsampled Shearlet, ultrasound image despeckling, Bayesian thresholding


Ultrasound images suffer of multiplicative noise named speckle. Bayesian shrinkage in transform domain is a well-known method based on finding threshold value to suppress the speckle noise. The main problem of applying Bayesian shrinkage is finding the optimum threshold value in appropriate transform domain. In this paper, we compare the performance of adaptive Bayesian thresholding when nonsubsampled Wavelet, Contourlet and Shearlet transforms are used. We processed two synthetic test images and three original ultrasound images as well to demonstrate the efficiency of the designed filters. In order to compare the performance of Bayesian shrinkage when employing the three mentioned transform domain, we used peak signal to noise ratio (PSNR), mean square error (MSE), and structural similarity (SSIM) as the full-reference (FR) objective criteria parameters and noise variance (NV), mean square difference (MSD), and equivalent number of looks (ENL) as the no-reference (NR) objective criteria parameters.

Cite This Paper

Sedigheh Ghofrani,"Comparing Nonsubsampled Wavelet, Contourlet and Shearlet Transforms for Ultrasound Image Despeckling", IJIGSP, vol.7, no.2, pp.15-22, 2015. DOI: 10.5815/ijigsp.2015.02.03


[1]M.C. Motwani, M.C. Gadiya, R.C. Motwani, and F. C. Harris, “Survey of image denoising techniques,” Proceedings of Global Signal Processing Expo., pp. 27-30, 2004. 

[2]N. Minh, V. Martin, “The Contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. on Image Processing, vol. 14, pp. 1-16, 2005.

[3]M. N. Do, and M. Vetterli, “Contourlets: a directional multiresolution image representation,” International Conference on Image Processing, pp. 357-360, 2002. 

[4]G. Easley, D. Labate, and W. Q. Lim, “Sparse directional image representations using the discrete shearlet transform,” Elsevier, Applied and Computational Harmonic Analysis vol. 25, no. 1, pp. 25-46, 2008.

[5]G. R. Easley, D. Labate, W. Q. Lim, “Optimally sparse image representations using shearlets,” 40th Asilomar Conference on Signals, Systems and Computers, pp. 974- 978, 2006.

[6]R. R. Coifman and D. L. Donoho, “Translation invariant de-noising”, Springer New York, Wavelets and Statistics. Lecture Notes in Statistics, pp. 125-150, 1995.

[7]M. Lang, H. Guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Processing Letters, vol. 3, no. 1, pp. 10- 12, 1996.

[8]A. L. Cunha, J. Zhou, and M. N. Do, “The Nonsubsampled Contourlet transform: theory, design and applications,” IEEE Trans. on Image Processing, vol. 15, no. 10, pp. 3089-3101, 2006.

[9]B. Hou, X. Z. Zhang, X. Bu, and H. Feng, “SAR image despeckling based on nonsubsampled shearlet transform,” IEEE, Journal of Selected Topics in Applied earth Observations and Remote Sensing, vol. 5, no. 3, pp. 809- 823, 2012.

[10]G. Andria, F. Attivissimo, M. L. Lanzolla, and M. Savino, “A suitable threshold for speckle reduction in ultrasound images,” IEEE Trans. on Instrumentation and Measurement, vol. 62, no. 8, pp. 2270- 2279, 2013.

[11]R. K. Rai, J. Asnani and T. R. Sontakke, “Review of shrinkage techniques for image denoising”, International Journal of Computer Application, vol. 42, no. 19, pp. 13-16, 2012.

[12]A. Pizurica, A. M. Wink, E. Vansteenkiste, W. Philips, and J. B.T.M. Roerdink, “A review of wavelet denoising in MRI and ultrasound brain imaging,” Bentham Science, Journal of Current Medical Imaging Reviews, vol. 2, no. 2, pp. 247- 260, 2006. 

[13]Z. Chen, X. Hao, Z. Sun, “Image denoising in shearlet domain by adaptive thresholding,” Journal of Information and Computational Science, vol. 10, no. 12, pp. 3741-3749, 2013.

[14]Y. S. Kim, and J. B. Ra, “Improvement of ultrasound image based on wavelet transform: speckle reduction and edge enhancement,” SPIE, Medical Imaging, vol. 5747, pp. 1085- 1092, 2005.

[15]R. Serhunadh, and T. Tessamma, “Spatially adaptive image denoising using undecimated directionlet transform,” International Journal of Computer Applications, vol. 84, no. 11, pp. 43- 49, 2013.

[16]A. Ouahabi, “A review of wavelet denoising in medical imaging,” IEEE, 8th International Workshop on Systems, Signal Processing, and their Applications, pp. 19- 26, 2013.

[17]D. L. Donoho and I. M. Johnstone, “Ideal spatial adaption via wavelet shrinkage”, Biometrika, vol. 81, no.3, pp. 425-455, 1994.

[18]D.L. Donoho, I.M. Johnstone, “Adapting to unknown smoothness via wavelet shrinkage”, Journal of the American Statistical Association, vol. 90, no. 432, pp. 1200–1224, 1995.

[19]D. X. Zhang, Q. W. Gao and X. P. Wu, “Bayesian-based speckle suppression for SAR image using Contourlet transform”, Journal of electronic science and technology of china, vol. 6, no. 1, pp. 79-82, 2008.

[20]H. A. Chipman, E. D. Kolaczyk, and R. E. McCulloch, "Adaptive Bayesian wavelet shrinkage," Journal of the American Statistical Association, vol. 92, pp. 1413-1421, 1997.