Skip to main content

Ultrasound speckle reduction using adaptive wavelet thresholding

Abstract

Ultrasound is the most widely used biomedical imaging modality for the purpose of diagnosis. It often comes with speckle that results in reduced quality of images by hiding fine details like edges and boundaries, as well as texture information. In this present study, a novel wavelet thresholding technique for despeckling of ultrasound images is proposed. For analysing performance of the method, it is first tested on synthetic (ground truth) images. Speckle noise with distinct noise levels (0.01–0.04) has been added to the synthetic images in order to examine its efficiency at different noise levels. The proposed technique is applied to various orthogonal and biorthogonal wavelet filters. It has been observed that Daubechies 1 gives the best results out of all wavelet filters. The proposed method is further applied on ultrasound images. Performance of the proposed technique has been validated by comparing it with some state-of-the-art techniques. The results have also been validated visually by the expert. Results reveal that the proposed technique outperforms other state-of-the-art techniques in terms of edge preservation and similarities in structures. Thus, the technique is effective in reducing speckle noise in addition to preserving texture information that can be used for further processing.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Abbott, J. G., & Thurston, F. L. (1979). Acoustic speckle: Theory and experimental analysis. Ultrasonic Imaging, 1(4), 303–324.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. Achim, A., Bezerianos, A., & Tsakalides, P. (2001). Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Transactions on Medical Imaging, 20(8), 772–783.

    Article  Google Scholar 

  4. Alkishriwo, O. A., & Algarguri, D. E. (2021). Ultrasound image speckle reduction based on adaptive image decomposition algorithm. In IEEE 1st international maghreb meeting of the conference on sciences and techniques of automatic control and computer engineering MI-STA.

  5. Alkishriwo, O. A. S. (2020). Image compression using adaptive multiresolution image decomposition algorithm. IET Image Processing, 14(14), 3572–3578.

    Article  Google Scholar 

  6. 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.

    Article  Google Scholar 

  7. Andria, G., Attivissimo, F., Lanzolla, A. M., & Savino, M. (2013). A suitable threshold for speckle reduction in ultrasound images. IEEE Transactions on Instrumentation and Measurement, 62(8), 2270–2279.

    Article  Google Scholar 

  8. Baselice, F. (2017). Ultrasound image despeckling based on statistical similarity. Ultrasound in Medicine & Biology, 43(9), 2065–2078.

    Article  Google Scholar 

  9. Bedi, A. K., Sunkaria, R. K., & Mittal, D. (2019). Ultrasound image despeckling and enhancement using modified multiscale anisotropic diffusion model in non-subsampled shearlet domain. The Computer Journal, 6, 66.

    Google Scholar 

  10. Burckhardt, C. B. (1978). Speckle in ultrasound B-mode scans. IEEE Transactions on Sonics and Ultrasonics, 25(1), 1–6.

    Article  Google Scholar 

  11. Chang, S. G., Yu, B., & Vetterli, M. (2000). Adaptive wavelet thresholding for image denoising and compression. EEE Transactions on Image Processing, 9(9), 1532–1546.

    MathSciNet  MATH  Article  Google Scholar 

  12. 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 

  13. Coupé, P., Hellier, P., Kervrann, C., & Barillot, C. (2009). Nonlocal means-based speckle filtering for ultrasound images. IEEE Transactions on Image Processing, 18(10), 2221–2229.

    MathSciNet  MATH  Article  Google Scholar 

  14. Devi, P. N., & Asokan, R. (2014). An improved adaptive wavelet shrinkage for ultrasound despeckling. Sadhana, 39(4), 971–988.

    Article  Google Scholar 

  15. Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, 12(1), 16–28.

    MathSciNet  MATH  Article  Google Scholar 

  16. Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.

    MathSciNet  MATH  Article  Google Scholar 

  17. Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224.

    MathSciNet  MATH  Article  Google Scholar 

  18. Firoiu, K., 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.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. Gupta, S., Anand, R. S., & Tyagi, B. (2014). Speckle filtering of ultrasound images using a modified non-linear diffusion model in non-subsampled shearlet domain. IET Image Processing, 9(2), 107–117.

    Article  Google Scholar 

  21. 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.

    Article  Google Scholar 

  22. Hiller, A. D., & Chin, R. T. (1991). Iterative wiener filters for image restoration. IEEE Transactions on Signal Processing, 39(8), 1892–1899.

    Article  Google Scholar 

  23. Kuan, D. T., Sawchuk, A., Strand, T. C., & Chavel, P. (1987). Adaptive restoration of image with speckle. IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(3), 373–383.

    Article  Google Scholar 

  24. Labate, D., Lim, W.-Q., Kutyniok, G., & Weiss, G. (2005). Sparse multidimensional representation using shearlets. In Wavelets XI, SPIE, San Diego, California, United States.

  25. Lee, J.-S. (1986). Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, 25(5), 170–179.

    Article  Google Scholar 

  26. 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.

    Article  Google Scholar 

  27. 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.

    MATH  Article  Google Scholar 

  28. Marchi, L. D., Testoni, N., & Speciale, N. (2006). Prostate tissue characterization via ultrasound speckle statistics. In 2006 IEEE international symposium on signal processing and information technology, Vancouver.

  29. 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.

    Article  Google Scholar 

  30. Mittal, D., Kumar, V., Saxena, S. C., Khandelwal, N., & Kalra, N. (2010). Enhancement of the ultrasound images by modified anisotropic diffusion method. Medical & Biological Engineering & Computing, 48(12), 1281–1291.

    Article  Google Scholar 

  31. Nasri, M., & Nezamabadi-pour, H. (2009). Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing, 72(4), 1012–1025.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. Pratt, W. K. (1991). Digital image processing (pp. 307–446). Wiley.

  34. 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.

    Article  Google Scholar 

  35. Randhawa, S. K., Sunkaria, R. K., & Puthooran, E. (2019). Despeckling of ultrasound images using novel adaptive wavelet thresholding function. Multidimensional Systems and Signal Processing, 30(3), 1545–1561.

    MATH  Article  Google Scholar 

  36. Sattar, F., Floreby, L., Salomonsson, G., & Lovstrom, B. (1997). Image enhancement based on a nonlinear multiscale method. IEEE Transactions on Image Processing, 6(6), 888–895.

    Article  Google Scholar 

  37. Shruthi, G., Usha, B. S., & Sandya, S. (2012). A novel approach for speckle reduction and enhancement of ultrasound images. International Journal of Computer Applications, 45(20), 14–20.

    Google Scholar 

  38. Starck, J.-L., Candès, E. J., & Donoho, D. L. (2002). The curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(6), 670–684.

    MathSciNet  MATH  Article  Google Scholar 

  39. Stein, M. (1981). Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 66, 1135–1151.

    MathSciNet  MATH  Google Scholar 

  40. 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.

    Article  Google Scholar 

  41. Wagner, R. F. (1983). Statistics of speckle in ultrasound B-scans. IEEE Transactions on Sonics & Ultrasonics, 30(3), 156–163.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  44. Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11), 1260–1270.

    MathSciNet  Article  Google Scholar 

  45. 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.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Anterpreet Kaur Bedi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bedi, A.K., Sunkaria, R.K. Ultrasound speckle reduction using adaptive wavelet thresholding. Multidim Syst Sign Process (2021). https://doi.org/10.1007/s11045-021-00799-4

Download citation

Keywords

  • Speckle reduction
  • Wavelet transform
  • Thresholding
  • Ultrasound