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Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO
The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013629/ https://www.ncbi.nlm.nih.gov/pubmed/35464187 http://dx.doi.org/10.1016/j.bspc.2022.103707 |
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author | Wang, Guowei Guo, Shuli Han, Lina Cekderi, Anil Baris Song, Xiaowei Zhao, Zhilei |
author_facet | Wang, Guowei Guo, Shuli Han, Lina Cekderi, Anil Baris Song, Xiaowei Zhao, Zhilei |
author_sort | Wang, Guowei |
collection | PubMed |
description | The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best. |
format | Online Article Text |
id | pubmed-9013629 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90136292022-04-18 Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO Wang, Guowei Guo, Shuli Han, Lina Cekderi, Anil Baris Song, Xiaowei Zhao, Zhilei Biomed Signal Process Control Article The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best. Elsevier Ltd. 2022-07 2022-04-18 /pmc/articles/PMC9013629/ /pubmed/35464187 http://dx.doi.org/10.1016/j.bspc.2022.103707 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Wang, Guowei Guo, Shuli Han, Lina Cekderi, Anil Baris Song, Xiaowei Zhao, Zhilei Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title | Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title_full | Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title_fullStr | Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title_full_unstemmed | Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title_short | Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO |
title_sort | asymptomatic covid-19 ct image denoising method based on wavelet transform combined with improved pso |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013629/ https://www.ncbi.nlm.nih.gov/pubmed/35464187 http://dx.doi.org/10.1016/j.bspc.2022.103707 |
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