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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