Despeckling of ultrasound images using novel adaptive wavelet thresholding function

  • Simarjot Kaur RandhawaEmail author
  • Ramesh Kumar Sunkaria
  • Emjee Puthooran


In the present work, a new thresholding function has been proposed for despeckling of ultrasound images. The main limitation of ultrasound images is presence of speckle noise which degrades image quality and hampers interpretation of the image. The proposed method has been first tested on the synthetic images so to analyse the performance of the proposed technique. The synthetic images are degraded by adding speckle noise with different degrees of noise variance (0.01–0.2) so as to analyse its performance for various noise variances. The proposed method is tested for orthogonal and biorthogonal wavelet filters. It is observed that Symlet 8 outperforms the other wavelet filters. The value of parameter ‘β’ is varied from 1 to 100 and its optimal value is selected which gives best results. Comparison with already existing exponential thresholding method, universal thresholding method, wiener filter and sparse coding have been made and proposed technique has given improved results. This method is tested on liver ultrasound images as well.


Ultrasound image Speckle noise Thresholding Noise variance Wavelet coefficients Despeckling 



The authors are grateful to the Ministry of Human Resource and Development, Government of India and Medical Imaging and Computational Modelling of Physiological Systems Research Laboratory, Department of Electronics and Communication Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab (India) for providing every type of financial, technical and administrative help to present this work.


  1. Abd-Elmoniem, K. Z., Youssef, A.-B. M., & Kadah, Y. M. (2002). Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transactions on Biomedical Engineering, 49(9), 997–1014.CrossRefGoogle Scholar
  2. Achim, A., Bezerianos, A., & Tsakalide, P. (2001). Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging, 20(8), 772–783.CrossRefGoogle Scholar
  3. Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions Signal Processing, 54(11), 4311–4322.CrossRefGoogle Scholar
  4. Aiazzi, B., Alparone, L., & Baronti, S. (1998). Multiresolution local-statistics speckle filtering based on a ratio Laplacian pyramid. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1466–1476.CrossRefGoogle Scholar
  5. Andria, G., Attivissimo, F., Cavone, G., Giaquinto, N., & Lanzolla, A. M. L. (2012). Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Measurement, 45(7), 1792–1800.CrossRefGoogle Scholar
  6. Andria, G., Attivissimo, F., Lanzolla, A. M. L., & Savino, M. (2012). Wavelet based methods for speckle reduction in ultrasound images. In 2012 IEEE international instrumentation and measurement technology conference (I2MTC) (pp. 1722–1725).Google Scholar
  7. Andria, G., Attivissimo, F., Lanzolla, A. M. L., & Savino, M. (2013). A suitable threshold for speckle reduction in ultrasound images. IEEE Transactions on Instrumentation and Measurement, 62(8), 2270–2279.CrossRefGoogle Scholar
  8. Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010). Multiplicative noise removal using variable splitting and constrained optimization. IEEE Transactions on Image Processing, 19(7), 1720–1730.MathSciNetCrossRefGoogle Scholar
  9. Burckhardt, C. B. (1978). Speckle in ultrasound B-mode scans. IEEE Transactions on Sonics and Ultrasonics, 25(1), 1–6.CrossRefGoogle Scholar
  10. Chang, S. G., Yu, B., & Vetterli, M. (2000). Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing, 9(9), 1532–1546.MathSciNetCrossRefGoogle Scholar
  11. Cho, D., & Bui, T. D. (2005). Multivariate statistical modeling for image denoising using wavelet transforms. Signal Processing: Image Communication, 20(1), 77–89.Google Scholar
  12. Choi, H. H., Lee, J. H., Kim, S. M., & Park, S. Y. (2015). Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique. Bio-Medical Materials and Engineering, 26(s1), S1587–S1597.CrossRefGoogle Scholar
  13. Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactins on Information Theory, 41(3), 613–627.MathSciNetCrossRefGoogle Scholar
  14. Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224.MathSciNetCrossRefGoogle Scholar
  15. Donohol, M. J., & Johnsone, M. (1994). Ideal spatial adaptation via wavelet shrinkage. Biometrika, 12(8), 430–445.Google Scholar
  16. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transaction Image Processing, 15(12), 3736–3745.MathSciNetCrossRefGoogle Scholar
  17. Firoiu, I., Nafornita, C., Boucher, J. M., & Isar, A. (2009). Image denoising using a new implementation of the hyperanalytic wavelet transform. IEEE Transactions on Instrumentation and Measurement, 58(8), 2410–2416.CrossRefGoogle Scholar
  18. Frost, V. S., Stiles, J. A., Shanmugan, K. S., & Holtzman, J. C. (1982). A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 157–166.CrossRefGoogle Scholar
  19. Gupta, S., Chauhan, R. C., & Saxena, S. C. (2004). Wavelet-based statistical approach for speckle reduction in medical ultrasound images. Medical and Biological Engineering and Computing, 42(2), 189–192.CrossRefGoogle Scholar
  20. Hao, X., Gao, S., & Gao, X. (1999). A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Transactions on Medical Imaging, 18(9), 787–794.CrossRefGoogle Scholar
  21. Hillery, A. D., & Chin, R. T. (1991). Iterative Wiener filters for image restoration. IEEE Transactions on Signal Processing, 39(8), 1892–1899.CrossRefGoogle Scholar
  22. Kuan, D., Sawchuk, A., Strand, T., & Chavel, P. (1987). Adaptive restoration of images with speckle. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(3), 373–383.CrossRefGoogle Scholar
  23. Lee, J. S. (1981). Speckle analysis and smoothing of synthetic aperture radar images. Computer graphics and image processing, 17(1), 24–32.CrossRefGoogle Scholar
  24. Loupas, T., McDicken, W. N., & Allan, P. L. (1989). An adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Transactions on Circuits and Systems, 36(1), 129–135.CrossRefGoogle Scholar
  25. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693.CrossRefGoogle Scholar
  26. Mateo, J. L., & Fernández-Caballero, A. (2009). Finding out general tendencies in speckle noise reduction in ultrasound images. Expert Systems with Applications, 36(4), 7786–7797.CrossRefGoogle Scholar
  27. Nasri, M., & Nezamabadi-pour, H. (2009). Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing, 72(4), 1012–1025.CrossRefGoogle Scholar
  28. Pizurica, A., Philips, W., Lemahieu, I., & Acheroy, M. (2003). A versatile wavelet domain noise filtration technique for medical imaging. IEEE Transactions on Medical Imaging, 22(3), 323–331.CrossRefGoogle Scholar
  29. Rabbani, H., Vafadust, M., Abolmaesumi, P., & Gazor, S. (2008). Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors. IEEE Transactions on Biomedical Engineering, 55(9), 2152–2160.CrossRefGoogle Scholar
  30. Rubinstein, R., Zibulevsky, M., & Elad, M. (2008). Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit. CS Technion report CS-2008-08. Computer Science Department, Technion.Google Scholar
  31. Stein, C. M. (1981). Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 9, 1135–1151.MathSciNetCrossRefGoogle Scholar
  32. Sudha, S., Suresh, G. R., & Sukanesh, R. (2009). Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. International Journal of Computer Theory and Engineering, 1(1), 7.CrossRefGoogle Scholar
  33. Wagner, R. F., Smith, S. W., Sandrik, J. M., & Lopez, H. (1983). Statistics of speckle in ultrasound B-scans. IEEE Transactions on Sonics and Ultrasonics, 30(3), 156–163.CrossRefGoogle Scholar
  34. Wang, X. Y., & Fu, Z. K. (2010). A wavelet-based image denoising using least squares support vector machine. Engineering Applications of Artificial Intelligence, 23(6), 862–871.CrossRefGoogle Scholar
  35. Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. EEE Transactions on Image Processing, 11(11), 1260–1270.MathSciNetCrossRefGoogle Scholar
  36. Zhang, F., Yoo, Y. M., Koh, L. M., & Kim, Y. (2007). Nonlinear diffusion in Laplacian pyramid domain for ultrasonic speckle reduction. IEEE Transactions on Medical Imaging, 26(2), 200–211.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Simarjot Kaur Randhawa
    • 1
    Email author
  • Ramesh Kumar Sunkaria
    • 1
  • Emjee Puthooran
    • 2
  1. 1.Dr. B. R. Ambedkar National Institute of TechnologyJalandharIndia
  2. 2.Jaypee University of Information TechnologySolanIndia

Personalised recommendations