莱斯校正的NLM算法在扩散加权图像中的应用
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摘要
扩散张量成像技术是一种非侵入活体获取脑白质结构的技术,其广泛应用于人体大脑的神经纤维跟踪.扩散张量图像是由扩散加权图像计算得到的,而扩散加权图像对噪声较为敏感,从而影响后续处理.扩散加权图像具有两个特点,一是图像的自相似性程度高,纹理和结构具有重复出现的特性且细节纹理较多,二是图像中所含噪声为莱斯噪声.基于这两个特点,我们提出了莱斯校正的非局部均值滤波算法.并将此算法应用于扩散加权图像的降噪中.算法首先针对图像中的莱斯噪声进行莱斯校正,然后再对校正后的图像使用非局部均值滤波器对其进行降噪.为了验证本文算法,通过实验将本文算法与传统的降噪算法进行比较.实验结果表明,本文算法能够更有效的减少扩散加权图像中的噪声,更好的保存了图像的纹理细节,提高了数据准确度.
Diffusion tensor imaging is a noninvasive technique to acquire white matter structures of the brain,which is widely used in the tracking of nerve fibers in the human brain. Diffusion tensor images are calculated by diffusion-weighted images. Diffusion-weighted images are more sensitive to noise,thus this will affect subsequent processing. Diffusion-weighted images have two characteristics.One is the high degree of self-similarity of the images,and the rich feature details. The other is that the noise contained in the image is Rician noise. Based on these characteristics,a Rician correction non-local means filter algorithm is proposed for the diffusion-weighted images denoising. And the proposed algorithm is applied to the diffusion-weighted images denoising. Firstly,the algorithm performs a Rician correction on the Rician noise in the images,and secondly it uses the non-local mean filter to reduce the noise on the corrected image. In order to verify our algorithm,the proposed algorithm is compared with some traditional denoising algorithms through experiments. The experimental results showthat the proposed algorithm can not only reduce the noise in the diffusion-weighted images,but also preserve much better the image local structures. The accuracy of data are improved.
Diffusion tensor imaging is a noninvasive technique to acquire white matter structures of the brain,which is widely used in the tracking of nerve fibers in the human brain. Diffusion tensor images are calculated by diffusion-weighted images. Diffusion-weighted images are more sensitive to noise,thus this will affect subsequent processing. Diffusion-weighted images have two characteristics.One is the high degree of self-similarity of the images,and the rich feature details. The other is that the noise contained in the image is Rician noise. Based on these characteristics,a Rician correction non-local means filter algorithm is proposed for the diffusion-weighted images denoising. And the proposed algorithm is applied to the diffusion-weighted images denoising. Firstly,the algorithm performs a Rician correction on the Rician noise in the images,and secondly it uses the non-local mean filter to reduce the noise on the corrected image. In order to verify our algorithm,the proposed algorithm is compared with some traditional denoising algorithms through experiments. The experimental results showthat the proposed algorithm can not only reduce the noise in the diffusion-weighted images,but also preserve much better the image local structures. The accuracy of data are improved.
引文
[1]Bao L J,Robini M,Liu W Y,et al.Structure-adaptive sparse denoising for diffusion-tensor M RI[J].M edical Image Analysis,2013,17(4):442-457.
[2]Liu M Z,Vemuri B C,Deriche R.A robust variational approach for simultaneous smoothing and estimation of DTI[J].NeuroImage,2013,67(2):33-41.
[3]Zhang Rui,Feng Xiang-chu,Yang Li-xia,et al.Global sparse gradient coupled tensor diffusion model for image denoising[J].Journal of Xidian University(Natural Science Edition),2017,44(6):150-155.
[4]Haldar J P,Wedeen V J,Nezamzadeh M,et al.Improved diffusion imaging through SNR-enhancing joint reconstruction[J].M agnetic Resonance in M edicine,2013,69(1):277-289.
[5]Zhang X Y,Peng J,Xu M,et al.Denoise diffusion-weighted images using higher-order singular value decomposition[J].NeuroImage,2017,156(8):128-145.
[6]Lam F,Babacan S D,Haldar J P,et al.Denoising diffusion-weighted M R magnitude image sequences using low rank and edge constraints[C].9th IEEE International Symposium on Biomedical Imaging,Barcelona,Spain:IEEE,2012:1401-1404.
[7]Lam F,Babacan S D,Haldar J P,et al.Denoising diffusion-weighted magnitude M R images using rank and edge constraints[J].M agnetic Resonance in M edicine,2014,71(3):1272-1284.
[8]Lam F,Liu D,Song Z,et al.A fast algorithm for denoising magnitude diffusion-w eighted images w ith rank and edge constraints[J].M agnetic Resonance in M edicine,2016,75(1):433-440.
[9]Mcgraw T,Vemuri B C,Chen Y,et al.DT-MRI denoising and neuronal fiber tracking[J].M edical Image Analysis,2004,8(2):95-111.
[10]Zhang Xiang-fen.Study on DTI image denoising[D].Shanghai:Shanghai Jiao Tong University,2008.
[11]Song Min-liang.Research on the nerve fibers of DTI imaging tracer system[D].Heilongjiang:Harbin Institute of Technology,2014.
[12]Zhang Jun.Brain fiber tracking algorithm with flow line differential equations based on neighborhood dictionary basis model[D].Zhejiang:Zhejiang University of Technology,2017.
[13]Basser P J,Mattiello J,LeBihan D.MR diffusion tensor spectroscopy and imaging[J].Biophysical Journal,1994,66(1):259-267.
[14]Stejskal E O,Tanner J E.Spin diffusion measurements:spin echoes in the presence of a time-dependent field gradient[J].The Journal of Chemical Physics,1965,42(1):288-292.
[15]Maximov I I,Grinberg F,Shah N J.Robust tensor estimation in diffusion tensor imaging[J].Journal of M agnetic Resonance,2011,213(1):136-144.
[16]Gudbjartsson H,Patz S.The rician distribution of noisy mri data[J].Magnetic Resonance in Medicine,2010,34(6):910-914.
[17]Zhang Jun-yi.The application of Rician factor estimation in the error analysis of direction finding[J].M easurement and Control Technology,2005,35(5):34-35.
[18]Wu Shao-huai.About Rayleigh distribution[J].Journal of Changzhou Institute of Technology,1986,(1):117-121.
[19]Miller K S,Bernstein R I,Blumenson L E.Generalized rayleigh processes[J].Quarterly of Applied M athematics,1958,169(2):137-145.
[20]Martin-Fernandez M,Mu1oz-Moreno E,Cammoun L,et al.Sequential anisotropic multichannel Wiener filtering w ith Rician bias correction applied to 3D regularization of DWI data[J].M edical Image Analysis,2009,13(1):19-35.
[21]Buades A,Coll B,Morel J M.A Non-local algorithm for image denoising[C].IEEE Computer Society Conference on Computer Vision&Pattern Recognition,IEEE Computer Society,2005:60-65.
[22]Rafsanjani H K,Sedaaghi M H,Saryazdi S.An adaptive diffusion coefficient selection for image denoising[J].Digital Signal Processing,2017,64(C):71-82.
[23]Kim K H,Ronen I,Formisano E,et al.Robust fiber tracking method by vector selection criterion in diffusion tensor images[C].Proceeding of the 26thAnnual International Conference of Engineering in M edicine and Biology Society,San Francisco,CA,USA:IEEE,2004:1080-1083.
[3]张瑞,冯象初,杨丽霞,等.全局稀疏梯度耦合张量扩散的图像去噪模型[J].西安电子科技大学学报(自然科学版),2017,44(6):150-155.
[10]张相芬.DTI图像去噪方法研究[D].上海:上海交通大学,2008.
[11]宋民亮.基于DTI成像的神经纤维示踪系统的研究[D].黑龙江:哈尔滨工业大学,2014.
[12]张军.基于邻域字典基模型的脑纤维流线微分方程跟踪算法[D].浙江:浙江工业大学,2017.
[17]张君毅.莱斯因子估计在测向误差分析中的应用[J].测控技术,2005,35(5):34-35.
[18]吴少怀.关于瑞利分布[J].常工院报学术论文集,1986(1):117-121.
1http://www.mathworks.com/matlabcentral/fileexchange/21130-dti-and-fiber-tracking
[2]Liu M Z,Vemuri B C,Deriche R.A robust variational approach for simultaneous smoothing and estimation of DTI[J].NeuroImage,2013,67(2):33-41.
[3]Zhang Rui,Feng Xiang-chu,Yang Li-xia,et al.Global sparse gradient coupled tensor diffusion model for image denoising[J].Journal of Xidian University(Natural Science Edition),2017,44(6):150-155.
[4]Haldar J P,Wedeen V J,Nezamzadeh M,et al.Improved diffusion imaging through SNR-enhancing joint reconstruction[J].M agnetic Resonance in M edicine,2013,69(1):277-289.
[5]Zhang X Y,Peng J,Xu M,et al.Denoise diffusion-weighted images using higher-order singular value decomposition[J].NeuroImage,2017,156(8):128-145.
[6]Lam F,Babacan S D,Haldar J P,et al.Denoising diffusion-weighted M R magnitude image sequences using low rank and edge constraints[C].9th IEEE International Symposium on Biomedical Imaging,Barcelona,Spain:IEEE,2012:1401-1404.
[7]Lam F,Babacan S D,Haldar J P,et al.Denoising diffusion-weighted magnitude M R images using rank and edge constraints[J].M agnetic Resonance in M edicine,2014,71(3):1272-1284.
[8]Lam F,Liu D,Song Z,et al.A fast algorithm for denoising magnitude diffusion-w eighted images w ith rank and edge constraints[J].M agnetic Resonance in M edicine,2016,75(1):433-440.
[9]Mcgraw T,Vemuri B C,Chen Y,et al.DT-MRI denoising and neuronal fiber tracking[J].M edical Image Analysis,2004,8(2):95-111.
[10]Zhang Xiang-fen.Study on DTI image denoising[D].Shanghai:Shanghai Jiao Tong University,2008.
[11]Song Min-liang.Research on the nerve fibers of DTI imaging tracer system[D].Heilongjiang:Harbin Institute of Technology,2014.
[12]Zhang Jun.Brain fiber tracking algorithm with flow line differential equations based on neighborhood dictionary basis model[D].Zhejiang:Zhejiang University of Technology,2017.
[13]Basser P J,Mattiello J,LeBihan D.MR diffusion tensor spectroscopy and imaging[J].Biophysical Journal,1994,66(1):259-267.
[14]Stejskal E O,Tanner J E.Spin diffusion measurements:spin echoes in the presence of a time-dependent field gradient[J].The Journal of Chemical Physics,1965,42(1):288-292.
[15]Maximov I I,Grinberg F,Shah N J.Robust tensor estimation in diffusion tensor imaging[J].Journal of M agnetic Resonance,2011,213(1):136-144.
[16]Gudbjartsson H,Patz S.The rician distribution of noisy mri data[J].Magnetic Resonance in Medicine,2010,34(6):910-914.
[17]Zhang Jun-yi.The application of Rician factor estimation in the error analysis of direction finding[J].M easurement and Control Technology,2005,35(5):34-35.
[18]Wu Shao-huai.About Rayleigh distribution[J].Journal of Changzhou Institute of Technology,1986,(1):117-121.
[19]Miller K S,Bernstein R I,Blumenson L E.Generalized rayleigh processes[J].Quarterly of Applied M athematics,1958,169(2):137-145.
[20]Martin-Fernandez M,Mu1oz-Moreno E,Cammoun L,et al.Sequential anisotropic multichannel Wiener filtering w ith Rician bias correction applied to 3D regularization of DWI data[J].M edical Image Analysis,2009,13(1):19-35.
[21]Buades A,Coll B,Morel J M.A Non-local algorithm for image denoising[C].IEEE Computer Society Conference on Computer Vision&Pattern Recognition,IEEE Computer Society,2005:60-65.
[22]Rafsanjani H K,Sedaaghi M H,Saryazdi S.An adaptive diffusion coefficient selection for image denoising[J].Digital Signal Processing,2017,64(C):71-82.
[23]Kim K H,Ronen I,Formisano E,et al.Robust fiber tracking method by vector selection criterion in diffusion tensor images[C].Proceeding of the 26thAnnual International Conference of Engineering in M edicine and Biology Society,San Francisco,CA,USA:IEEE,2004:1080-1083.
[3]张瑞,冯象初,杨丽霞,等.全局稀疏梯度耦合张量扩散的图像去噪模型[J].西安电子科技大学学报(自然科学版),2017,44(6):150-155.
[10]张相芬.DTI图像去噪方法研究[D].上海:上海交通大学,2008.
[11]宋民亮.基于DTI成像的神经纤维示踪系统的研究[D].黑龙江:哈尔滨工业大学,2014.
[12]张军.基于邻域字典基模型的脑纤维流线微分方程跟踪算法[D].浙江:浙江工业大学,2017.
[17]张君毅.莱斯因子估计在测向误差分析中的应用[J].测控技术,2005,35(5):34-35.
[18]吴少怀.关于瑞利分布[J].常工院报学术论文集,1986(1):117-121.
1http://www.mathworks.com/matlabcentral/fileexchange/21130-dti-and-fiber-tracking