基于变分的CT图像环形伪影校正

霍其润; 李建武; 陆耀; 秦明

doi:10.16383/j.aas.c180258

基于变分的CT图像环形伪影校正

doi: 10.16383/j.aas.c180258

霍其润^1,,
李建武^2, ,,
陆耀^2,,
秦明^3,

1.
首都师范大学信息工程学院北京 100048
2.
北京理工大学计算机学院北京 100081
3.
中国电力科学研究院有限公司北京 100192

基金项目:

北京市教委科研计划 KM201810028016

详细信息

作者简介:
霍其润博士, 首都师范大学信息工程学院讲师.主要研究方向为图像处理, 计算机视觉, 机器学习.E-mail:huoqirun@cnu.edu.cn

陆耀北京理工大学计算机学院教授.主要研究方向为图像和信号处理, 模式识别, 神经网络.E-mail:vis_yl@bit.edu.cn

秦明博士, 中国电力科学研究院有限公司工程师.主要研究方向为图像与信号处理, 机器学习, 模式识别.E-mail:qinming@epri.sgcc.com.cn

通讯作者:
李建武北京理工大学计算机学院副教授.主要研究方向为图像处理, 超分辨率图像重建技术.本文通信作者.E-mail:ljw@bit.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2018-04-26
- 录用日期: 2018-10-06
- 刊出日期: 2019-09-20

Variation-based Ring Artifact Correction in CT Images

HUO Qi-Run^1
,,
LI Jian-Wu^{2
, ,},
LU Yao^2
,,
QIN Ming^3
,

1.
College of Information Engineering, Capital Normal University, Beijing 100048
2.
School of Computer Science, Beijing Institute of Technology, Beijing 100081
3.
China Electric Power Research Institute, Beijing 100192

Funds:

Scientific Research Program of Beijing Educational Committee KM201810028016

More Information

Author Bio:
Ph.D., lecturer at the College of Information Engineering, Capital Normal University. Her research interest covers image processing, computer vision and machine learning

Professor at the School of Computer Science, Beijing Institute of Technology. His research interest covers image and signal processing, pattern recognition and neural network

Ph.D., engineer of China Electric Power Research Institute. His research interest covers image and signal processing, machine learning and pattern recognition

Corresponding author: LI Jian-Wu Associate professor at the School of Computer Science, Beijing Institute of Technology. His research interest covers image processing and super-resolution image reconstruction. Corresponding author of this paper

摘要

摘要: 有效去除CT图像中环形伪影是医学图像处理领域的一个重要研究方向，现有的方法在去除环形伪影的同时，对CT图像的边缘及细节保留存在困难和挑战.本文采用变分优化的思想，将环形伪影的去除问题建模为一个能量最小化问题，来缓解保持图像信息和去除伪影之间的矛盾，提出了一种后处理的伪影校正算法.根据环形伪影产生机理和特性表现构造有针对性的变分模型，一是从环形伪影的几何特性入手，设计更为合理的梯度保真形式，增强模型对图像细节信息的保护；二是从环形伪影的边缘特性入手，构建具有伪影辨识能力的相对全变分正则项，降低模型对图像结构性信息的影响.基于构造的变分模型，采用高效的优化求解算法，实现环形伪影的有效去除.对比实验表明，无论在视觉观察还是定量分析方面，本文算法均体现出了较好的性能.
- CT图像 /
- 环形伪影 /
- 后处理 /
- 变分模型
Abstract: Effective removal of ring artifacts in CT images is an important research area in the field of medical image processing. The existing methods have some difficulties and challenges in preserving the edges and details of CT images while removing ring artifacts. In this paper, we model the removal of ring artifacts as a problem of energy minimization based on the idea of variational optimization and propose a post-processing method to alleviate the conflict between maintaining image information and removing artifacts. The variational model has been constructed according to the analysis of the mechanism and characteristics of ring artifacts. On the one hand, we design a more suitable gradient fidelity term based on the geometrical characteristics of ring artifacts to enhance the protection on image details. On the other hand, we construct a relative total variational regular term with the ability of artifact identification based on the edge characteristics of ring artifacts, so as to reduce influences on the structural information of images. The designed variational model is to be solved by an efficient optimization algorithm to effectively remove ring artifacts. Experimental results show that the proposed method presents better performance on both visual inspection and quantitative assessment.
- CT image /
- ring artifact /
- post-processing /
- variational model
注释:

1) 本文责任编委张道强

HTML全文

图 1 本文的校正算法流程示意图

Fig. 1 The flow chart of the proposed method

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图 2 极坐标转换

Fig. 2 Polar coordinate transformation

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图 3 边缘剖面示意图

Fig. 3 Edge profile diagram

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图 4 两类边缘产生的梯度效果示意图

Fig. 4 Gradient diagram of two types of edges

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图 5 Shepp-Logan图像处理结果

Fig. 5 Experimental results on the Shepp-Logan phantom

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图 6 Lena图像处理结果

Fig. 6 Experimental results on the Lena image

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图 7 脑部CT图像处理结果

Fig. 7 Experiments on a brain CT image

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图 8 颈部CT图像处理结果

Fig. 8 Experiments on a neck CT image

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图 9 脑部CT图像中的ROI选取

Fig. 9 A brain CT image with ROIs

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图 10 图像伪影去除效果对比

Fig. 10 Comparison of artifact removal effects on the Lena image

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图 11 颈部CT图像伪影去除效果对比

Fig. 11 Comparison of artifact removal effects on the neck CT image

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图 12 Lena图像上TV和RTV的约束效果对比

Fig. 12 Comparison of constraint effects between TV and RTV on the Lena image

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图 13 颈部CT图像上TV和RTV的约束效果对比

Fig. 13 Comparison of constraint effects between TV and RTV on neck CT image

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图 14 算法迭代的收敛性趋势

Fig. 14 Convergence of iteration algorithm

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表 1 各算法结果的图像质量评价指标值

Table 1 Quantitative comparison for the different methods

算法	Shepp-Logan图像		Lena图像
算法	PSNR	MSSIM	PSNR	MSSIM
WF算法	36.4504	0.8841	36.0376	0.9897
RCP算法	42.5464	0.8925	36.0161	0.9888
VDM算法	43.7735	0.9010	37.0608	0.9945
本文算法	49.0341	0.9679	37.9287	0.9966

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表 2 各算法结果相应局部区域(图 9)的LSNR值

Table 2 LSNRs of the ROIs circled in Fig. 9 for different methods

图像	LSNR
图像	ROI1	ROI2	ROI3
原始图像	39.3519	46.1822	46.5832
WF算法结果	45.0595	49.1965	47.6961
RCP算法结果	44.4747	48.4365	47.3001
VDM算法结果	45.4732	46.7967	48.7736
本文算法结果	49.8897	49.8348	49.6774

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参考文献(32)

[1]	Szyszko T A, Gjr C. PET/CT and PET/MRI in head and neck malignancy. Clinical Radiology, 2018, 73(1):60-69 https://www.ncbi.nlm.nih.gov/pubmed/29029767
[2]	韩光辉, 刘峡壁, 郑光远.肺部CT图像病变区域检测方法.自动化学报, 2017, 43(12):2071-2090 http://www.aas.net.cn/CN/abstract/abstract19182.shtml Han Guang-Hui, Liu Xia-Bi, Zheng Guang-Yuan. Automated detection of lesion regions in lung computed tomography images:a review. Acta Automatica Sinica, 2017, 43(12):2071-2090 http://www.aas.net.cn/CN/abstract/abstract19182.shtml
[3]	江孝国, 张开志, 李成刚, 王远.图像平场校正方法的扩展应用研究.光子学报, 2007, 36(9):1587-1590 http://d.old.wanfangdata.com.cn/Periodical/gzxb200709006 Jiang Xiao-Guo, Zhang Kai-Zhi, Li Cheng-Gang, Wang Yuan. Extended applications of image flat-field correction method. Acta Photonica Sinica, 2007, 36(9):1587-1590 http://d.old.wanfangdata.com.cn/Periodical/gzxb200709006
[4]	傅健, 路宏年.扇束X射线ICT中环状伪影的一种校正方法.光学精密工程, 2002, 10(6):542-546 doi: 10.3321/j.issn:1004-924X.2002.06.002 Fu Jian, Lu Hong-Nian. Correcting method for ring artifacts in fan-beam X-ray ICT. Optics and Precision Engineering, 2002, 10(6):542-546 doi: 10.3321/j.issn:1004-924X.2002.06.002
[5]	Raven C. Numerical removal of ring artifacts in microtomography. Review of scientific instruments, 1998, 69(8):2978-2980 doi: 10.1063/1.1149043
[6]	Münch B, Trtik P, Marone F, et al. Stripe and ring artifact removal with combined wavelet-Fourier filtering. Optics Express, 2009, 17(10):8567-8591 doi: 10.1364/OE.17.008567
[7]	Ashrafuzzaman A N M, Lee S Y, Hasan M K. A self-adaptive approach for the detection and correction of stripes in the sinogram:suppression of ring artifacts in CT imaging. EURASIP Journal on Advances in Signal Processing, 2011, 2011(1):1-13 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_a074aeaf4d859728152788be8a7939c6
[8]	Hasan M K, Sadi F, Lee S Y. Removal of ring artifacts in micro-CT imaging using iterative morphological filters. Signal, Image and Video Processing, 2012, 6(1):41-53 doi: 10.1007/s11760-010-0170-z
[9]	Anas E M, Lee S Y, Hasan K. Classification of ring artifacts for their effective removal using type adaptive correction schemes. Computers in Biology and Medicine, 2011, 41(6):390-401 doi: 10.1016/j.compbiomed.2011.03.018
[10]	Rashid S, Lee S Y, Hasan M K. An improved method for the removal of ring artifacts in high resolution CT imaging. EURASIP Journal on Advances in Signal Processing, 2012, 2012(1):93 doi: 10.1186/1687-6180-2012-93
[11]	Kim Y, Baek J, Hwang D. Ring artifact correction using detector line-ratios in computed tomography. Optics express, 2014, 22(11):13380-13392 doi: 10.1364/OE.22.013380
[12]	Miqueles E X, Rinkel J, O'Dowd F, et al. Generalized Titarenko's algorithm for ring artefacts reduction. Journal of Synchrotron Radiation, 2014, 21(6):1333-1346 doi: 10.1107/S1600577514016919
[13]	Titarenko V. Analytical formula for two-dimensional ring artefact suppression. Journal of Synchrotron Radiation, 2016, 23(6):1447-1461 doi: 10.1107/S160057751601479X
[14]	Paleo P, Mirone A. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of Synchrotron Radiation, 2015, 22(5):1268-1278 doi: 10.1107/S1600577515010176
[15]	Sijbers J, Postnov A. Reduction of ring artefacts in high resolution. Physics in Medicine and Biology, 2004, 49(14):N247 doi: 10.1088/0031-9155/49/14/N06
[16]	Prell D, Kyriakou Y, Kalender W A. Comparison of ring artifact correction methods for flat-detector CT. Physics in medicine and biology, 2009, 54(12):3881-3895 doi: 10.1088/0031-9155/54/12/018
[17]	Wei Z, Wiebe S, Chapman D. Ring artifacts removal from synchrotron CT image slices. Journal of Instrumentation, 2013, 8(06):C06006 doi: 10.1088/1748-0221/8/06/C06006
[18]	Yan L, Wu T, Zhong S, Zhang Q. A variation-based ring artifact correction method with sparse constraint for flat-detector CT. Physics in medicine and biology, 2016, 61(3):1278-1292 doi: 10.1088/0031-9155/61/3/1278
[19]	Huo Q, Li J, Lu Y, Yan Z. Removing ring artifacts in CBCT images using smoothing based on relative total variation. In:Proceedings of the 2016 International Conference on Neural Information Processing. Kyoto, Japan:Springer, Cham, 2016. 501-509
[20]	Huo Q, Li J, Lu Y, Yan Z. Removing ring artifacts in CBCT images via L0 smoothing. International Journal of Imaging Systems and Technology, 2016, 26(4):284-294 doi: 10.1002/ima.22200
[21]	Liang X, Zhang Z, Niu T, Yu S, Wu S, Li Z, et al. Iterative image-domain ring artifact removal in cone-beam CT. Physics in Medicine and Biology, 2017, 62(13):5276-5292 doi: 10.1088/1361-6560/aa7017
[22]	Tang S, Gong W, Li W, Wang W. Non-blind image deblurring method by local and nonlocal total variation models. Signal Processing, 2014, 94(1):339-349 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=39e15af175cb22de05ca634f1611d3e6
[23]	李权合, 毕笃彦, 许悦雷, 查宇飞.雾霾天气下可见光图像场景再现.自动化学报, 2014, 40(4):744-750 http://www.aas.net.cn/CN/abstract/abstract18340.shtml Li Quan-He, Bi Du-Yan, Xu Yue-Lei, Zha Yu-Fei. Haze degraded image scene rendition. Acta Automatica Sinica, 2014, 40(4):744-750 http://www.aas.net.cn/CN/abstract/abstract18340.shtml
[24]	Kim Y, Vese L A. Image recovery using functions of bounded variation and Sobolev spaces of negative differentiability. Inverse Problems and Imaging, 2017, 3(1):43-68 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=662bb3848cc767c2bb159a4e62148848
[25]	张桂梅, 孙晓旭, 刘建新, 等.基于分数阶微分的TV-L1光流模型的图像配准方法研究.自动化学报, 2017, 43(12):2213-2224 http://www.aas.net.cn/CN/abstract/abstract19194.shtml Zhang Gui-Mei, Sun Xiao-Xu, Liu Jian-Xin, et al. Research on TV-L1 optical flow model for image registration based on fractional-order differentiation. Acta Automatica Sinica, 2017, 43(12):2213-2224 http://www.aas.net.cn/CN/abstract/abstract19194.shtml
[26]	Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D Nonlinear Phenomena, 1992, 60(1-4):259-268 doi: 10.1016/0167-2789(92)90242-F
[27]	Bouali M, Ladjal S. Toward optimal destriping of MODIS data using a unidirectional variational model. IEEE Transactions on geoscience and remote sensing, 2011, 49(8):2924-2935 doi: 10.1109/TGRS.2011.2119399
[28]	Zhang H, He W, Zhang L, Shen H, Yuan Q. Hyperspectral image restoration using low-rank matrix recovery. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8):4729-4743 doi: 10.1109/TGRS.2013.2284280
[29]	Chang Y, Yan L, Fang H, Luo C. Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping. IEEE Transactions on Image Processing, 2015, 24(6):1852-1866 doi: 10.1109/TIP.2015.2404782
[30]	Chan T F. Aspects of total variation regularized L1 function approximation. Siam Journal on Applied Mathematics, 2005, 65(5):1817-1837 doi: 10.1137/040604297
[31]	Micchelli C A. Proximity algorithms for image models Ⅱ:L1/TV denoising. Advances in Computational Mathematics, 2011, 38:401-426 doi: 10.1007/s10444-011-9243-y
[32]	Xu L, Yan Q, Xia Y, Jia J. Structure extraction from texture via relative total variation. ACM Transactions on Graphics, 2012, 31(6):139-148 http://d.old.wanfangdata.com.cn/Periodical/dbch201901051