Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising

Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented...

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Main Authors:	Ai, Danni, Yang, Jian, Fan, Jingfan, Cong, Weijian, Wang, Yongtian
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436221/

id	pubmed-4436221
recordtype	oai_dc
spelling	pubmed-44362212015-05-27 Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising Ai, Danni Yang, Jian Fan, Jingfan Cong, Weijian Wang, Yongtian Research Article Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods. Public Library of Science 2015-05-18 /pmc/articles/PMC4436221/ /pubmed/25993566 http://dx.doi.org/10.1371/journal.pone.0126914 Text en © 2015 Ai et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Ai, Danni Yang, Jian Fan, Jingfan Cong, Weijian Wang, Yongtian
spellingShingle	Ai, Danni Yang, Jian Fan, Jingfan Cong, Weijian Wang, Yongtian Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
author_facet	Ai, Danni Yang, Jian Fan, Jingfan Cong, Weijian Wang, Yongtian
author_sort	Ai, Danni
title	Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
title_short	Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
title_full	Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
title_fullStr	Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
title_full_unstemmed	Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising
title_sort	adaptive tensor-based principal component analysis for low-dose ct image denoising
description	Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4436221/
_version_	1613225123620323328

Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising

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