Lightweight digital imaging and communications in medicine image encryption for IoT system

Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, th...

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Bibliographic Details
Published in:Telkomnika 2023-08, Vol.21 (4), p.771-783
Main Authors: Hatem, Muntaha Abdulzahra, Hameedi, Balsam Abdulkadhim, Hasoon, Jamal Nasir
Format: Article
Language:English
Subjects:
Algorithms
Communication networks
Computed tomography
Coronaviruses
Correlation analysis
COVID-19
Cryptography
Data encryption
Digital imaging
Encryption
Entropy (Information theory)
Error analysis
Health care facilities
Image enhancement
Internet of Things
Lightweight
Magnetic resonance imaging
Medical imaging
Noise intensity
Patients
Privacy
Security
Signal to noise ratio
Storage systems
Technology assessment
Viral diseases
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Summary:Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for "digital imaging and communications in medicine". The efficiency of the proposed system is proved in terms of adjacent pixels' correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.
ISSN:1693-6930
2302-9293
DOI:10.12928/telkomnika.v21i4.24766