基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法
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摘要
超声成像是现代医学影像学最重要的诊断技术之一。然而,由于乘性斑点噪声的存在,使得超声成像的发展受到了一定的限制。针对这种问题,提出了一种贝叶斯非局部平均(NLM)滤波算法的改进策略。首先,运用贝叶斯公式推导出适应于超声图像斑点噪声模型的非局部平均滤波器,由此引出了两种图像块之间距离计算的方式——Pearson距离和根距离;其次,为了减轻计算负担,在非局部区域中选取相似图像块时采用图像块预选择的方式来加速算法;另外,根据多次实验,总结出了一种滤波参数和噪声方差的关系,实现了参数的自适应;最后,利用Visual Studio和OpenCV实现了算法,使得程序的运行时间大幅缩短。为了评估所提算法的去噪性能,在幻影图像和真实超声图像上进行了实验,结果表明:与现有的一些经典算法相比,该算法在去除斑点噪声的表现上有很大提升,并且在保留图像边缘和结构细节方面取得了令人满意的结果。
Ultrasound imaging is one of the most important diagnostic techniques of modern medical imaging. However,due to the presence of multiplicative speckle noise, the development of ultrasound imaging has been limited. For this problem,an improved strategy for Bayesian Non-Local Means( NLM) filtering algorithm was proposed. Firstly,a Bayesian formulation was applied to derive an NLM filter adapted to a relevant ultrasound noise model, which leads to two methods of calculating distance between the image blocks, the Pearson distance and the root distance. Secondly, to lighten the computational burden,a image block pre-selection process was used to accelerate the algorithm when a similar image block was selected in the nonlocal area. In addition, the relationship between parameter and noise variance was determined experimentally, which made the parameter being adaptive to the noise. Finally, the VS(Visual Studio) and OpenCV(Open source Computer Visual library)were used to realize the algorithm, making the program running time greatly reduced. In order to evaluate the denoising performance of the proposed algorithm, experiments were conducted on both phantom images and real ultrasound images. The experimental results show that the algorithm has a great improvement in the performance of removing speckle noise and achieves satisfactory results in terms of preserving the edges and image details, compared with some existing classical algorithms.
Ultrasound imaging is one of the most important diagnostic techniques of modern medical imaging. However,due to the presence of multiplicative speckle noise, the development of ultrasound imaging has been limited. For this problem,an improved strategy for Bayesian Non-Local Means( NLM) filtering algorithm was proposed. Firstly,a Bayesian formulation was applied to derive an NLM filter adapted to a relevant ultrasound noise model, which leads to two methods of calculating distance between the image blocks, the Pearson distance and the root distance. Secondly, to lighten the computational burden,a image block pre-selection process was used to accelerate the algorithm when a similar image block was selected in the nonlocal area. In addition, the relationship between parameter and noise variance was determined experimentally, which made the parameter being adaptive to the noise. Finally, the VS(Visual Studio) and OpenCV(Open source Computer Visual library)were used to realize the algorithm, making the program running time greatly reduced. In order to evaluate the denoising performance of the proposed algorithm, experiments were conducted on both phantom images and real ultrasound images. The experimental results show that the algorithm has a great improvement in the performance of removing speckle noise and achieves satisfactory results in terms of preserving the edges and image details, compared with some existing classical algorithms.
引文
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[21]张兆伦.基于非局部均值图像去噪算法研究[D].南京:南京邮电大学,2015:19-24.(ZHANG Z L.The research on image denoising algorithm based on non-local mean[D].Nangjing:Nangjing University of Posts and Telecommunications,2015:19-24.)
[22]周颖玥,臧红彬,赵井坤,等.基于非局部平均滤波的冲击噪声图像恢复算法[J].计算机应用研究,2016,33(11):3489-3494.(ZHOU Y Y,ZANG H B,ZHAO J K,et al.Image recovering algorithm for impulse noise based on nonlocal means filter[J].Application Research of Computers,2016,33(11):3489-3494.)
[23]SUDEEP P V,PALANISAMY P,RAJAN J,et al.Speckle reduction in medical ultrasound images using an unbiased non-local means method[J].Biomedical Signal Processing and Control,2016,28(6):1-8.
[24]KERVRANN C,BOULANGER J,COUPP.Bayesian non-local means filter,image redundancy and adaptive dictionaries for noise removal[C]//International Conference on Scale Space and Variational Methods in Computer Vision,LNCS 4485.Berlin:Springer,2007:520-532.
[25]BUADES A,COLL B,MOREL J M.A review of image denoising algorithms,with a new one[J].SIAM Journal on Multiscale Modeling and Simulation,2010,4(2):490-530.
[26]BUADES A,COLL B,MOREL J.On image denoising methods[EB/OL].[2017-03-12].http://www.ece.lsu.edu/gunturk/EE7700/On Image Denoising Methods.pdf.
[27]COUPE P,HELLIER P,KERVRANN C,et al.Nonlocal meansbased speckle filtering for ultrasound images[J].IEEE Transactions on Image Processing,2009,18(10):2221-2229.
[28]SPJOTVOLL E.A nonlinear gaussian filter applied to images with discontinuities[J].Journal of Nonparametric Statistics,1997,8(1):21-43.
[29]MAHMOUDI M,SAPIRO G.Fast image and video denoising via nonlocal means of similar neighborhoods[J].IEEE Signal Processing Letters,2005,12(12):839-842.
[2]刘春明,张相芬,陈武凡.基于小波的医学超声图像斑点噪声抑制方法[J].中国医学物理学杂志,2006,23(5):364-367.(LIUC M,ZHANG X F,CHEN W F.Wavelet-based method for speckle reduction in medical ultrasound image[J].Chinese Journal of Medical Physics,2006,23(5):364-367.)
[3]LEE J S.Digital image enhancement and noise filtering by use of local statistics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1980,2(2):165-168.
[4]FROST V S,STILES J A,SHANMUGAN K S,et al.A model for radar images and its application to adaptive digital filtering of multiplicative noise[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1982,4(2):157-166.
[5]KUAN D T,SAWCHUK A A,STRAND T C,et al.Adaptive noise smoothing filter for images with signal-dependent noise[J].IEEETransactions on Pattern Analysis and Machine Intelligence,1985,7(2):165-177.
[6]赵高长,张磊,武风波.改进的中值滤波算法在图像去噪中的应用[J].应用光学,2011,32(4):678-682.(ZHAO G C,ZHANGL,WU F B.Application of improved median filtering algorithm to image de-noising[J].Journal of Applied Optics,2011,32(4):678-682.)
[7]胡静波.改进的中值滤波去噪算法分析[J].信息技术,2011(8):32-33.(HU J B.Analysis of improved median filtering de-noising algorithm[J].Information Technology,2011(8):32-33.)
[8]LOPES A,TOUZI R,NEZRY E.Adaptive speckle filters and scene heterogeneity[J].IEEE Transactions on Geoscience and Remote Sensing,1990,28(6):992-1000.
[9]KARAMAN M,KUTAY M A,BOZDAGI G.An adaptive speckle suppression filter for medical ultrasonic imaging[J].IEEE Transactions on Medical Imaging,1995,14(2):283-292.
[10]PERONA P,MALIK J.Scale-space and edge detection using anisotropic diffusion[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,12(7):629-639.
[11]RAMOS-LLORDEN G,VEGAS-SANCHEZ-FERRERO G,MAR-TIN-FERNANDEZ M,et al.Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images[J].IEEETransactions on Image Processing,2015,24(1):345-358.
[12]RUDIN L I,OSHER S,FATEMI E.Nonlinear total variation based noise removal algorithms[J].Physica D:Nonlinear Phenomena,1992,60(1/2/3/4):259-268.
[13]洪志强,张立亭,陈竹安,等.基于改进的各向异性扩散图像去噪算法研究[J].江西科学,2017,35(3):433-437.(HONG ZQ,ZHANG L T,CHEN Z A,et al.Research of images denoising based on improved anisotropic diffusion algorithm[J].Jiangxi Science,2017,35(3):433-437.)
[14]YU Y,MOLLOY J A,ACTON S T.Three-dimensional speckle reducing anisotropic diffusion[C]//Proceedings of the 2003 Conference Record of the 37th Asilomar Conference on Signals,Systems and Computers.Piscataway,NJ:IEEE,2003:1987-1991.
[15]RABBANI H,VAFADUST M,ABOLMAESUMI P,et al.Speckle noise reduction of medical ultrasound images in complex wavelet domain using mixture priors[J].IEEE Transactions on Biomedical Engineering,2008,55(9):2152-2160.
[16]NI C,LI Q,XIA L Z.A novel method of infrared image denoising and edge enhancement[J].Signal Processing,2008,88(6):1606-1614.
[17]ACHIM A,BEZERIANOS A,TSAKALIDES P.Novel Bayesian multiscale method for speckle removal in medical ultrasound images[J].IEEE Transactions on Medical Imaging,2001,20(8):772-783.
[18]KHARE A,KHARE M,JEONG Y,et al.Despeckling of medical ultrasound images using Daubechies complex wavelet transform[J].Signal Processing,2010,90(2):428-439.
[19]赵井坤,周颖玥,林茂松.基于稀疏表示与非局部相似的图像去噪算法[J].计算机应用,2016,36(2):551-555.(ZHAO J K,ZHOU Y Y,LIN M S.Image denoising algorithm based on sparse representation and nonlocal similarity[J].Journal of Computer Applications,2016,36(2):551-555.)
[20]HARMELING S.Image denoising:can plain neural networks compete with BM3D?[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition.Washington,DC:IEEE Computer Society,2012:2392-2399.
[21]张兆伦.基于非局部均值图像去噪算法研究[D].南京:南京邮电大学,2015:19-24.(ZHANG Z L.The research on image denoising algorithm based on non-local mean[D].Nangjing:Nangjing University of Posts and Telecommunications,2015:19-24.)
[22]周颖玥,臧红彬,赵井坤,等.基于非局部平均滤波的冲击噪声图像恢复算法[J].计算机应用研究,2016,33(11):3489-3494.(ZHOU Y Y,ZANG H B,ZHAO J K,et al.Image recovering algorithm for impulse noise based on nonlocal means filter[J].Application Research of Computers,2016,33(11):3489-3494.)
[23]SUDEEP P V,PALANISAMY P,RAJAN J,et al.Speckle reduction in medical ultrasound images using an unbiased non-local means method[J].Biomedical Signal Processing and Control,2016,28(6):1-8.
[24]KERVRANN C,BOULANGER J,COUPP.Bayesian non-local means filter,image redundancy and adaptive dictionaries for noise removal[C]//International Conference on Scale Space and Variational Methods in Computer Vision,LNCS 4485.Berlin:Springer,2007:520-532.
[25]BUADES A,COLL B,MOREL J M.A review of image denoising algorithms,with a new one[J].SIAM Journal on Multiscale Modeling and Simulation,2010,4(2):490-530.
[26]BUADES A,COLL B,MOREL J.On image denoising methods[EB/OL].[2017-03-12].http://www.ece.lsu.edu/gunturk/EE7700/On Image Denoising Methods.pdf.
[27]COUPE P,HELLIER P,KERVRANN C,et al.Nonlocal meansbased speckle filtering for ultrasound images[J].IEEE Transactions on Image Processing,2009,18(10):2221-2229.
[28]SPJOTVOLL E.A nonlinear gaussian filter applied to images with discontinuities[J].Journal of Nonparametric Statistics,1997,8(1):21-43.
[29]MAHMOUDI M,SAPIRO G.Fast image and video denoising via nonlocal means of similar neighborhoods[J].IEEE Signal Processing Letters,2005,12(12):839-842.