Abstract
Ultrasound diagnostic techniques are widely used in medical clinical diagnostics. However, the presence of speckle noise in the ultrasound imaging process reduces the image quality and creates inconvenience to the physician during clinical diagnosis. The ability to reduce the influence of speckle noise has important significance therefore in medical ultrasound image diagnosis. This paper offers a solution. It proposes a novel despeckling method based on nonsubsampled shearlet transformation and a guided filter. First, a nonsubsampled Laplacian pyramid filter is used to decompose the noisy image thus decomposing the image into high-frequency and low-frequency subbands. Under the direction of the non-sampling filter bank, a high-frequency subband multi-directional decomposition is obtained. Next, based on the threshold function and the correlation of the shearlet coefficients in the transformation domain, an improved threshold shrinkage algorithm is proposed to perform the threshold shrinkage processing on the shearlet coefficients of the high-frequency subbands. Finally, the low-frequency subbands in the transformation domain are processed by the guided filter, and a denoised ultrasonic image is obtained by the inverse transformation of the shearlet. So as to verify the effectiveness of the proposed method, experiments were conducted, and the results were compared to those of other existing denoising filters. These showed the proposed method performs more effectively at denoising and delivers clearer image detail.
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References
S. Ajafernández, C. Alberolalópez, On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Image Proc. 15(9), 2694–2701 (2006)
G. Andria, F. Attivissimo, G. Cavone et al., Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images. Measurement 45, 1792–1800 (2012)
S. Balocco, C. Gatta, O. Pujol et al., SRBF: Speckle reducing bilateral filtering. Ultrasound Med. Biol. 36(8), 1353–1363 (2010)
P. Coupé, P. Hellier, C. Kervrann et al., Nonlocal means-based speckle filtering for ultrasound Images. IEEE Trans. Image Process. 18(10), 2221–2229 (2009)
A.L.D. Cunha, J. Zhu, M.N. Do, The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. 15, 3089–3101 (2006)
M.N. Do, M. Vetterli, The finite ridgelet transform for image representation. IEEE Trans. Image Process. 12, 16–28 (2003)
D.L. Donoho, I.M. Johnstone, Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)
G. Easley, D. Labate, W.Q. Lim, Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harm. Anal. 25, 25–46 (2008)
I. Elyasi, S. Zarmehi, Elimination noise by adaptive wavelet threshold. Eng. Technol. 6745(2), 462–466 (2009)
V. Frost, J. Stiles, K. Shanmugan et al., A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982)
K.H. Guo, D. Labate, Optimally sparse multidimensional representation using shearlets. SIAM J. Math. Anal. 39(1), 298–318 (2007)
K.H. Guo, D. Labate, W.Q. Lim, G. Weiss, E. Wilson, Wavelets with composite dilations and their MR A properties. Appl. Comput. Harmon. Anal. 20(2), 202–236 (2006)
H. Kaiming, S. Jian, T. Xiaoou, Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)
W. Kong, Y. Lei, Technique for image fusion between gray-scale visual light and infrared images based on NSST and improved RF. Optik 124, 6423–6431 (2013)
K. Krissian, C. Westin, R. Kikinis et al., Oriented speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 16(5), 1412–1424 (2007)
D. Labate, W.Q. Lim, G. Kutyniok, G. Weiss, Sparse multidimensional representation using shearlets. in Proceedings of the SPIE, pp. 254–262 (2005)
H. Li, B.S. Manjunath, S.K. Mitra, Multisensor image fusion using the wavelet transform. J. Graph. Model Image Process. 57, 235–245 (1995)
Q.G. Miao, C. Shi, P.F. Xu, M. Yang, Y.B. Shi, A novel algorithm of image fusion using shearlets. Opt. Commun. 284, 1540–1547 (2011)
G. Montaldo, M. Tanter, M. Fink, Time reversal of speckle noise. Phys. Rev. Lett. 106(5), 1–10 (2011)
S. Parrilli, M. Poderico, C.V. Angelino et al., A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012)
J.L. Starck, E.J. Candes, D.L. Donoho, The curvelet transform for image denoising. IEEE Trans. Image Process. 11, 670–684 (2002)
J. Zhang, G. Lin, Y. Cheng, Speckle filtering of medical ultrasonic images using wavelet and guided filter. Ultrasonics 65, 177–193 (2016)
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that help improve the manuscript. The authors would also like to thank the people who provide the MATLAB code or executable file for their filters. The work is partially supported by the Natural Science Foundation of Zhejiang Province, China (LQY18F030001).
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Zhang, J., Xiu, X., Zhou, J. et al. A Novel Despeckling Method for Medical Ultrasound Images Based on the Nonsubsampled Shearlet and Guided Filter. Circuits Syst Signal Process 39, 1449–1470 (2020). https://doi.org/10.1007/s00034-019-01201-2
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DOI: https://doi.org/10.1007/s00034-019-01201-2