CT image sequence restoration based on sparse and low-rank decomposition.

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...

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Autores principales:	Shuiping Gou, Yueyue Wang, Zhilong Wang, Yong Peng, Xiaopeng Zhang, Licheng Jiao, Jianshe Wu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b86e9173a114ea6bb9130806e93b4c9
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spelling	oai:doaj.org-article:8b86e9173a114ea6bb9130806e93b4c92021-11-18T08:57:02ZCT image sequence restoration based on sparse and low-rank decomposition.1932-620310.1371/journal.pone.0072696https://doaj.org/article/8b86e9173a114ea6bb9130806e93b4c92013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24023764/?tool=EBIhttps://doaj.org/toc/1932-6203Blurry 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.Shuiping GouYueyue WangZhilong WangYong PengXiaopeng ZhangLicheng JiaoJianshe WuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e72696 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuiping Gou Yueyue Wang Zhilong Wang Yong Peng Xiaopeng Zhang Licheng Jiao Jianshe Wu CT image sequence restoration based on sparse and low-rank decomposition.
description	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.
format	article
author	Shuiping Gou Yueyue Wang Zhilong Wang Yong Peng Xiaopeng Zhang Licheng Jiao Jianshe Wu
author_facet	Shuiping Gou Yueyue Wang Zhilong Wang Yong Peng Xiaopeng Zhang Licheng Jiao Jianshe Wu
author_sort	Shuiping Gou
title	CT image sequence restoration based on sparse and low-rank decomposition.
title_short	CT image sequence restoration based on sparse and low-rank decomposition.
title_full	CT image sequence restoration based on sparse and low-rank decomposition.
title_fullStr	CT image sequence restoration based on sparse and low-rank decomposition.
title_full_unstemmed	CT image sequence restoration based on sparse and low-rank decomposition.
title_sort	ct image sequence restoration based on sparse and low-rank decomposition.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/8b86e9173a114ea6bb9130806e93b4c9
work_keys_str_mv	AT shuipinggou ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT yueyuewang ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT zhilongwang ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT yongpeng ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT xiaopengzhang ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT lichengjiao ctimagesequencerestorationbasedonsparseandlowrankdecomposition AT jianshewu ctimagesequencerestorationbasedonsparseandlowrankdecomposition
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CT image sequence restoration based on sparse and low-rank decomposition.

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