Gabor-based anisotropic diffusion with lattice Boltzmann method for medical ultrasound despeckling

Haohao Xu; Yuchen Gong; Xinyi Xia; Dong Li; Zhuangzhi Yan; Jun Shi; Qi Zhang; Haohao Xu; Yuchen Gong; Xinyi Xia; Dong Li; Zhuangzhi Yan; Jun Shi; Qi Zhang

doi:10.3934/mbe.2019379

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 7546-7561. doi: 10.3934/mbe.2019379

Previous Article Next Article

Research article Special Issues

Gabor-based anisotropic diffusion with lattice Boltzmann method for medical ultrasound despeckling

1.
The SMART (Smart Medicine and AI-based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
2.
Institute of Biomedical Engineering, Shanghai University, Shanghai 200444, China
3.
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received: 02 April 2019 Accepted: 23 July 2019 Published: 19 August 2019

Medical ultrasound images are corrupted by speckle noise, and despeckling methods are required to effectively and efficiently reduce speckle noise while simultaneously preserving details of tissues. This paper proposes a despeckling approach named the Gabor-based anisotropic diffusion coupled with the lattice Boltzmann method (GAD-LBM), which uses the lattice Boltzmann method (LBM) to fast solve the partial differential equation of an anisotropic diffusion model embedded with the Gabor edge detector. We evaluated the GAD-LBM on both synthetic and clinical ultrasound images, and the experimental results suggested that the GAD-LBM was superior to other nine methods in speckle suppression and detail preservation. For synthetic and clinical images, the computation time of the GAD-LBM was about 1/90 to 1/20 of the GAD solved with the finite difference, indicating the advantage of the GAD-LBM in efficiency. The GAD-LBM not only has excellent ability of noise reduction and detail preservation for ultrasound images, but also has advantages in computational efficiency.
- despeckling,
- Gabor-based anisotropic diffusion,
- lattice Boltzmann method,
- computation time,
- ultrasound
Citation: Haohao Xu, Yuchen Gong, Xinyi Xia, Dong Li, Zhuangzhi Yan, Jun Shi, Qi Zhang. Gabor-based anisotropic diffusion with lattice Boltzmann method for medical ultrasound despeckling[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 7546-7561. doi: 10.3934/mbe.2019379

Related Papers:

Abstract

Medical ultrasound images are corrupted by speckle noise, and despeckling methods are required to effectively and efficiently reduce speckle noise while simultaneously preserving details of tissues. This paper proposes a despeckling approach named the Gabor-based anisotropic diffusion coupled with the lattice Boltzmann method (GAD-LBM), which uses the lattice Boltzmann method (LBM) to fast solve the partial differential equation of an anisotropic diffusion model embedded with the Gabor edge detector. We evaluated the GAD-LBM on both synthetic and clinical ultrasound images, and the experimental results suggested that the GAD-LBM was superior to other nine methods in speckle suppression and detail preservation. For synthetic and clinical images, the computation time of the GAD-LBM was about 1/90 to 1/20 of the GAD solved with the finite difference, indicating the advantage of the GAD-LBM in efficiency. The GAD-LBM not only has excellent ability of noise reduction and detail preservation for ultrasound images, but also has advantages in computational efficiency.

References

[1]	Y. H. Qian, D. D'Humières and P. Lallemand, Lattice BGK models for navier-stokes equation, Europhys. Lett., 17 (1992), 479.
[2]	T. Loupas, W. N. McDicken and P. L. Allan, An adaptive weighted median filter for speckle suppression in medical ultrasonic images, IEEE Trans. Circuits Syst., 36 (1989), 129–135.
[3]	V. S. Frost, J. A. Stiles, K. S. Shanmugan, et al., A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. Pattern Anal. Machine Intell., 2 (1982), 157–166.
[4]	J. S. Lee, Speckle suppression and analysis for synthetic aperture radar images, Opt. Eng., 25 (1986), 636–643.
[5]	P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Trans. Pattern Anal. Machine Intell., 12 (1990), 629–639.
[6]	J. Wu, Y. Wang, J. Yu, et al., Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images, J. Opt. Soc. Am. A, 32 (2015), 248–257.
[7]	W. Wojciech and P. Ewa, Granular filter in medical image noise suppression and edge preservation, Biocybern. Biomed. Eng., 39 (2019), 1–16.
[8]	D. Chen, S. Maclachlan and M. Kilmer, Iterative parameter-choice and multigrid methods for anisotropic diffusion denoising, Siam J. Sci. Comput., 33 (2011), 2972–2994.
[9]	E. J. Leavline and S. Sutha, Fast multiscale directional filter bank-based speckle mitigation in gallstone ultrasound images, J. Opt. Soc. Am. A, 31 (2014), 283–292.
[10]	Y. Wang, J. Z. Cheng, D. Ni, et al., Towards personalized statistical deformable model and hybrid point matching for robust MR-TRUS registration, IEEE Trans. Med. Imaging, 35 (2015), 589–604.
[11]	J. Liu, T. Z. Huang, Z. Xu, et al., High-order total variation-based multiplicative noise removal with spatially adapted parameter selection, J. Opt. Soc. Am. A, 30 (2013), 1956–1966.
[12]	Q. Zhang, H. Han, C. Ji, et al., Gabor-based anisotropic diffusion for speckle noise reduction in medical ultrasonography, J. Opt. Soc. Am. A, 31 (2014), 1273–1283.
[13]	Q. Huang, Y. Zheng, M. Lu, et al., A new adaptive interpolation algorithm for 3D ultrasound imaging with speckle reduction and edge preservation, Comput. Med. Imag. Graphics, 33 (2009), 100–110.
[14]	X. Min, Y. Zhang, H. D. Cheng, et al., Automatic breast ultrasound image segmentation: A survey, Pattern Recognit., 79 (2018), 340–355.
[15]	F. Higuera, S. Succi and R. Benzi, Lattice gas dynamics with enhanced collisions, Europhys. Lett., 9 (1989), 345.
[16]	R. Benzi, S. Succi and M. Vergassola, Theory and application of the lattice Boltzmann equation, Phys. Rep, 222 (1992), 147.
[17]	S. Succi, Lattice Boltzmann 2038, Europhys. Lett., 109 (2015), 50001.
[18]	X. Shan and H. Chen, Lattice Boltzmann model for simulating flows with multiple phases and components, Phys. Rev. E, 47 (1993), 1815.
[19]	S. Chen and G. D. Doolen, Lattice Boltzmann method for fluid flows, Annu. Rev. Fluid Mech., 30 (1998), 329–364.
[20]	Y. Zhao, Lattice Boltzmann based PDE solver on the GPU, Vis. Comput., 24 (2008), 323–333.
[21]	Q. Chang and T. Yang, A lattice Boltzmann method for image denoising, IEEE Trans. Image Process., 18 (2009), 2797–2802.
[22]	H. Chen, D. Ni, J. Qin, et al., Standard plane localization in fetal ultrasound via domain transferred deep neural networks, IEEE J. Biomed. Health Inform., 19 (2015), 1627–1636.
[23]	M. J. Lyons, J. Budynek and S. Akamatsu, Automatic classification of single facial images, IEEE Trans. Pattern Anal. Machine Intell., 21 (1999), 1357–1362.
[24]	C. Liu and H. Wechsler, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, IEEE Trans. Image Process., 11 (2002), 467–476.
[25]	Y. Yue, M. M. Croitoru, A. Bidani, et al., Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images, IEEE Trans. Med. Imaging, 25 (2006), 297–311.
[26]	B. Jawerth, P. Lin and E. Sinzinger, Lattice Boltzmann models for anisotropic diffusion of images, J. Math. Imaging Vis., 11 (1999), 231–237.
[27]	M. Watari, What is the small parameter ε in the Chapman-Enskog expansion of the Lattice Boltzmann method?, J. Fluids Eng.,134 (2012), 011401.
[28]	Y. Wang, C. S. Chua, Y. K. Ho, Face recognition from 2D and 3D images using 3D Gabor filters, Image Vis. Comput., 23 (2005), 1018–1028.
[29]	W. Zhang and B. Shi, Application of lattice Boltzmann method to image filtering, J. Math. Imaging Vis., 43 (2012), 135–142.
[30]	J. S. Lee, Speckle analysis and smoothing of synthetic aperture radar images, Comput. Graphics Image Process., 17 (1981), 24–32.
[31]	Y. Yu and S. T. Acton, Speckle reducing anisotropic diffusion, IEEE Trans. Image Process., 11 (2002), 1260–1270.
[32]	Y. Wu, B. Tracey, P. Natarajan, et al., James-Stein type center pixel weights for non-local means image denoising, IEEE Signal Process. Lett., 20 (2013), 411–414.
[33]	Z. Wang, A. C. Bovik, H. R. Sheikh, et al., Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13 (2004), 600–612.
[34]	A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one, Multisc. Model. Simulat., 4 (2005), 490–530.
[35]	Y. Zhang, H. D. Cheng, J. Huang, et al., An effective and objective criterion for evaluating the performance of denoising filters, Pattern Recognit., 45 (2012), 2743–2757.
[36]	J. Yu, J. Tan and Y. Wang, Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method, Pattern Recognit., 43 (2010), 3083–3092.
[37]	A. Buades, B. Coll, and J. M. Morel., A non-local algorithm for image denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2 (2005), 60–65. Avaliable from: https://ieeexplore.ieee.org/abstract/document/1467423.
[38]	G. Gilboa, N. Sochen, and Y. Y. Zeevi, Image enhancement and denoising by complex diffusion processes, IEEE Trans. Pattern Anal. Machine Intell., 26 (2004), 1020–1036.
[39]	S. Liang, F. Yang, T. Wen, et al., Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling, BMC Med. Imag., 17 (2017), 57.
[40]	X. Feng, X. Guo and Q. Huang, Systematic evaluation on speckle suppression methods in examination of ultrasound breast images, Applied Sci., 7 (2016), 37.
[41]	G. Falcucci, G. Bella, G. Chiatti, et al., Lattice Boltzmann models with mid-range interactions, Commun. Comput. Phys., 2 (2007), 1071–1084.
[42]	M. Sbragaglia, R. Benzi, L. Biferale, et al., Generalized lattice Boltzmann method with multirange pseudopotential, Phys. Rev. E, 75 (2007), 026702.
[43]	J. Zhang, E. Lou, X. Shi, et al., A computer-aided Cobb angle measurement method and its reliability, J. Spinal Disord. Tech., 23 (2010), 383–387.
[44]	Q. Huang, F. Zhang and X. Li, Machine learning in ultrasound computer-aided diagnostic systems: A survey, BioMed Res. Int., 2018 (2018), 1–10.

Reader Comments

Your name:*

Email:*
© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)