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Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System

Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an...

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Autores principales: Geetha, B. T., Mohan, Prakash, Mayuri, A. V. R., Jackulin, T., Aldo Stalin, J. L., Anitha, Varagantham
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377849/
https://www.ncbi.nlm.nih.gov/pubmed/35978898
http://dx.doi.org/10.1155/2022/2243827
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author Geetha, B. T.
Mohan, Prakash
Mayuri, A. V. R.
Jackulin, T.
Aldo Stalin, J. L.
Anitha, Varagantham
author_facet Geetha, B. T.
Mohan, Prakash
Mayuri, A. V. R.
Jackulin, T.
Aldo Stalin, J. L.
Anitha, Varagantham
author_sort Geetha, B. T.
collection PubMed
description Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques.
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spelling pubmed-93778492022-08-16 Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System Geetha, B. T. Mohan, Prakash Mayuri, A. V. R. Jackulin, T. Aldo Stalin, J. L. Anitha, Varagantham Comput Intell Neurosci Research Article Presently, technological advancements in the healthcare sector pose a challenging problem relevant to the security and privacy of health-related applications. Medical images can be considered significant and sensitive data in the medical informatics system. In order to transmit medical images in an open medium, the design of secure encryption algorithms becomes essential. Encryption can be considered one of the effective solutions for accomplishing security. Although numerous models have existed in the literature, they could not adaptable to the rising number of medicinal images in the health sector. At the same time, the optimal key generation process acts as a vital part in defining the performance of the encryption techniques. Therefore, this article presents a Pigeon Inspired Optimization with Encryption-based Secure Medical Image Management (PIOE-SMIM) technique. The proposed PIOE-SMIM approach majorly concentrates on the development of secret share creation (SSC) and the encryption process. At the initial stage, the medical images are converted into a collection of 12 shares using the SSC approach. In addition, an elliptic curve cryptography (ECC) scheme is employed for the encryption process. In order to optimum key creation procedure in the ECC model, the PIO technique is exploited with the aim of maximizing PSNR. Finally, on the receiver side, the decryption and share reconstruction processes are performed to construct the original images. The PIOE-SMIM model displayed an enhanced PSNR of 59.37 dB in image 1. Improved PSNR of 59.53 dB is given for image 5 using the PIOE-SMIM model. For demonstrating an enhanced performance of the PIOE-SMIM method, a widespread experimental study is made and the results highlighted the supremacy of the PIOE-SMIM model over other techniques. Hindawi 2022-08-08 /pmc/articles/PMC9377849/ /pubmed/35978898 http://dx.doi.org/10.1155/2022/2243827 Text en Copyright © 2022 B. T. Geetha et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Geetha, B. T.
Mohan, Prakash
Mayuri, A. V. R.
Jackulin, T.
Aldo Stalin, J. L.
Anitha, Varagantham
Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title_full Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title_fullStr Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title_full_unstemmed Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title_short Pigeon Inspired Optimization with Encryption Based Secure Medical Image Management System
title_sort pigeon inspired optimization with encryption based secure medical image management system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9377849/
https://www.ncbi.nlm.nih.gov/pubmed/35978898
http://dx.doi.org/10.1155/2022/2243827
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