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research-article

Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization

Authors:

Sameera V. Mohd Sagheer,

Sudhish N. GeorgeAuthors Info & Claims

Volume 94, Issue C

Pages 1 - 17

https://doi.org/10.1016/j.artmed.2018.12.006

Published: 01 March 2019 Publication History

Highlights

•

A new denoising method for Computed tomography (CT) images based on low-rank approximation by modeling the global spatial correlation and local smoothness properties is proposed.

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The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness.

•

An efficient algorithm for solving the resulting optimization problem based on the Alternative Direction Method of Multipliers (ADMM) is also developed.

•

The experiments explain the applicability of proposed method for real medical CT images.

•

The proposed algorithm is shown to have superior performance compared to the state-of-art works existing in the literature.

Abstract

Low-dose Computed Tomography (CT) imaging is a most commonly used medical imaging modality. Though the reduction in dosage reduces the risk due to radiation, it leads to an increase in noise level. Hence, it is a mandatory requirement to include a noise reduction technique as a pre- and/or post-processing step for better disease diagnosis. The nuclear norm minimization has attracted a great deal of research interest in contemporary years. This paper proposes a low-rank approximation based approach for denoising of CT images by effectively utilizing the global spatial correlation and local smoothness properties. The tensor nuclear norm is used to describe the global properties and the tensor total variation is used to characterize the local smoothness as well as to improve global smoothness. The resulting optimization problem is solved by the Alternative Direction Method of Multipliers (ADMM) technique. Experimental results on simulated and real CT data prove that the proposed methods outperform the state-of-art works.

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Cited By

Deng JHu ZHe JLiu JQiao GGu GWeng S(2024)Irregular feature enhancer for low-dose CT denoisingMultimedia Systems10.1007/s00530-024-01575-730:6Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1007/s00530-024-01575-7
Popa JLou YMinkoff S(2023)Low-Rank Tensor Data Reconstruction and Denoising via ADMM: Algorithm and Convergence AnalysisJournal of Scientific Computing10.1007/s10915-023-02364-697:2Online publication date: 7-Oct-2023
https://dl.acm.org/doi/10.1007/s10915-023-02364-6
Kulathilake KAbdullah NSabri ABandara ALai K(2022)A review on self-adaptation approaches and techniques in medical image denoising algorithmsMultimedia Tools and Applications10.1007/s11042-022-13511-w81:26(37591-37626)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13511-w

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Denoising of low-dose CT images via low-rank tensor modeling and total variation regularization

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Information & Contributors

Information

Published In

cover image Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Volume 94, Issue C

Mar 2019

153 pages

ISSN:0933-3657

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers Ltd.

United Kingdom

Publication History

Published: 01 March 2019

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Cited By

Deng JHu ZHe JLiu JQiao GGu GWeng S(2024)Irregular feature enhancer for low-dose CT denoisingMultimedia Systems10.1007/s00530-024-01575-730:6Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1007/s00530-024-01575-7
Popa JLou YMinkoff S(2023)Low-Rank Tensor Data Reconstruction and Denoising via ADMM: Algorithm and Convergence AnalysisJournal of Scientific Computing10.1007/s10915-023-02364-697:2Online publication date: 7-Oct-2023
https://dl.acm.org/doi/10.1007/s10915-023-02364-6
Kulathilake KAbdullah NSabri ABandara ALai K(2022)A review on self-adaptation approaches and techniques in medical image denoising algorithmsMultimedia Tools and Applications10.1007/s11042-022-13511-w81:26(37591-37626)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13511-w

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