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.
Similar content being viewed by others
References
Abbott, J. G., & Thurston, F. L. (1979). Acoustic speckle: Theory and experimental analysis. Ultrasonic Imaging, 1(4), 303–324.
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.
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.
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.
Alkishriwo, O. A. S. (2020). Image compression using adaptive multiresolution image decomposition algorithm. IET Image Processing, 14(14), 3572–3578.
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.
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.
Baselice, F. (2017). Ultrasound image despeckling based on statistical similarity. Ultrasound in Medicine & Biology, 43(9), 2065–2078.
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.
Burckhardt, C. B. (1978). Speckle in ultrasound B-mode scans. IEEE Transactions on Sonics and Ultrasonics, 25(1), 1–6.
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.
Cho, D., & Bui, T. D. (2005). Multivariate statistical modeling for image denoising using wavelet transforms. Signal Processing: Image Communication, 20(1), 77–89.
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.
Devi, P. N., & Asokan, R. (2014). An improved adaptive wavelet shrinkage for ultrasound despeckling. Sadhana, 39(4), 971–988.
Do, M. N., & Vetterli, M. (2003). The finite ridgelet transform for image representation. IEEE Transactions on Image Processing, 12(1), 16–28.
Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.
Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association, 90(432), 1200–1224.
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.
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.
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.
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.
Hiller, A. D., & Chin, R. T. (1991). Iterative wiener filters for image restoration. IEEE Transactions on Signal Processing, 39(8), 1892–1899.
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.
Labate, D., Lim, W.-Q., Kutyniok, G., & Weiss, G. (2005). Sparse multidimensional representation using shearlets. In Wavelets XI, SPIE, San Diego, California, United States.
Lee, J.-S. (1986). Speckle suppression and analysis for synthetic aperture radar images. Optical Engineering, 25(5), 170–179.
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.
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.
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.
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.
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.
Nasri, M., & Nezamabadi-pour, H. (2009). Image denoising in the wavelet domain using a new adaptive thresholding function. Neurocomputing, 72(4), 1012–1025.
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.
Pratt, W. K. (1991). Digital image processing (pp. 307–446). Wiley.
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.
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.
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.
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.
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.
Stein, M. (1981). Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 66, 1135–1151.
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.
Wagner, R. F. (1983). Statistics of speckle in ultrasound B-scans. IEEE Transactions on Sonics & Ultrasonics, 30(3), 156–163.
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.
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.
Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11), 1260–1270.
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bedi, A.K., Sunkaria, R.K. Ultrasound speckle reduction using adaptive wavelet thresholding. Multidim Syst Sign Process 33, 275–300 (2022). https://doi.org/10.1007/s11045-021-00799-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-021-00799-4