IntOPMICM: Intelligent Medical Image Size Reduction Model.
Journal
Journal of healthcare engineering
ISSN: 2040-2309
Titre abrégé: J Healthc Eng
Pays: England
ID NLM: 101528166
Informations de publication
Date de publication:
2022
2022
Historique:
received:
06
11
2021
accepted:
25
01
2022
entrez:
7
3
2022
pubmed:
8
3
2022
medline:
6
5
2022
Statut:
epublish
Résumé
Due to the increasing number of medical imaging images being utilized for the diagnosis and treatment of diseases, lossy or improper image compression has become more prevalent in recent years. The compression ratio and image quality, which are commonly quantified by PSNR values, are used to evaluate the performance of the lossy compression algorithm. This article introduces the IntOPMICM technique, a new image compression scheme that combines GenPSO and VQ. A combination of fragments and genetic algorithms was used to create the codebook. PSNR, MSE, SSIM, NMSE, SNR, and CR indicators were used to test the suggested technique using real-time medical imaging. The suggested IntOPMICM approach produces higher PSNR SSIM values for a given compression ratio than existing methods, according to experimental data. Furthermore, for a given compression ratio, the suggested IntOPMICM approach produces lower MSE, RMSE, and SNR values than existing methods.
Identifiants
pubmed: 35251570
doi: 10.1155/2022/5171016
pmc: PMC8896923
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5171016Informations de copyright
Copyright © 2022 Piyush Kumar Pareek et al.
Déclaration de conflit d'intérêts
The authors of this manuscript declare that they do not have any conflicts of interest.
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