Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Guowei, Guo, Shuli, Han, Lina, Cekderi, Anil Baris, Song, Xiaowei, Zhao, Zhilei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
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
_version_ 1784688035618619392
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
work_keys_str_mv AT wangguowei asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso
AT guoshuli asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso
AT hanlina asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso
AT cekderianilbaris asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso
AT songxiaowei asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso
AT zhaozhilei asymptomaticcovid19ctimagedenoisingmethodbasedonwavelettransformcombinedwithimprovedpso