IR@PKUHSC  > 北京大学临床肿瘤学院
学科主题临床医学
CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition
Gou, Shuiping1; Wang, Yueyue1; Wang, Zhilong2; Peng, Yong3; Zhang, Xiaopeng2; Jiao, Licheng1; Wu, Jianshe1
刊名PLOS ONE
2013-09-04
DOI10.1371/journal.pone.0072696
8期:9
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Multidisciplinary Sciences
研究领域[WOS]Science & Technology - Other Topics
英文摘要

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.

语种英语
WOS记录号WOS:000324515600037
项目编号K5051302027 ; 2013CB329402 ; B07048 ; 2011JQ8020
资助机构Fundamental Funds for the Center Universities ; National Basic Research Program (973 Program) of China ; Foreign Scholars in University Research and Teaching Programs (the 111 Project) ; Provincial Natural Science Foundation of Shaanxi of China
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/50014
专题北京大学临床肿瘤学院
作者单位1.Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian, Shaanxi, Peoples R China
2.Fourth Mil Med Univ, Xijing Hosp, Dept Radiol, Xian 710032, Shaanxi, Peoples R China
3.Peking Univ, Sch Oncol, Beijing Canc Hosp, Beijing 100871, Peoples R China
推荐引用方式
GB/T 7714
Gou, Shuiping,Wang, Yueyue,Wang, Zhilong,et al. CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition[J]. PLOS ONE,2013,8(9).
APA Gou, Shuiping.,Wang, Yueyue.,Wang, Zhilong.,Peng, Yong.,Zhang, Xiaopeng.,...&Wu, Jianshe.(2013).CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition.PLOS ONE,8(9).
MLA Gou, Shuiping,et al."CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition".PLOS ONE 8.9(2013).
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Gou, Shuiping]的文章
[Wang, Yueyue]的文章
[Wang, Zhilong]的文章
百度学术
百度学术中相似的文章
[Gou, Shuiping]的文章
[Wang, Yueyue]的文章
[Wang, Zhilong]的文章
必应学术
必应学术中相似的文章
[Gou, Shuiping]的文章
[Wang, Yueyue]的文章
[Wang, Zhilong]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。