An IoT-Based Electronic Health Protection Mechanism With AMBTC Compressed Images

Since the COVID-19 outbreak, there has been a growing need for contactless healthcare to meet medical diagnosis demands. Electronic health systems using the Internet of Things (IoT) are rapidly advancing, transmitting significant amounts of private medical data online. In telemedicine, where patients are diagnosed remotely, sensitive information, such as patient records, may be embedded into medical images for security purposes. Due to the large file sizes of medical images produced by equipment, compression is essential for fast transmission. To safeguard medical images in telemedicine and address bandwidth limitations, we utilize data hiding techniques and absolute moment block truncation coding (AMBTC) compression to introduce an IoT-driven electronic health protection mechanism. Our mechanism employs diverse methods to compress and embed data across various image blocks. Additionally, it offers a flexible adaptation to meet different application requirements concerning embedding capacity, visual quality, and file size by adjusting thresholds and variants. Compared to alternative methods, our approach delivers superior payload capacity and efficiency while preserving visual fidelity.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-09-30
DOI
https://doi.org/10.1109/jiot.2024.3467152
Citations
8
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
1.98

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article

An IoT-Based Electronic Health Protection Mechanism With AMBTC Compressed Images

Yijie Lin, Chin‐Chen Chang, Chia‐Chen Lin, Ching-Chun Chang
8 citations
IEEE Internet of Things Journal
Brain Tumor Detection and Classification
1.98
article

An IoT-Based Electronic Health Protection Mechanism With AMBTC Compressed Images

Yijie Lin, Chin‐Chen Chang, Chia‐Chen Lin, Ching-Chun Chang
article en
8 citations

Abstract

Since the COVID-19 outbreak, there has been a growing need for contactless healthcare to meet medical diagnosis demands. Electronic health systems using the Internet of Things (IoT) are rapidly advancing, transmitting significant amounts of private medical data online. In telemedicine, where patients are diagnosed remotely, sensitive information, such as patient records, may be embedded into medical images for security purposes. Due to the large file sizes of medical images produced by equipment, compression is essential for fast transmission. To safeguard medical images in telemedicine and address bandwidth limitations, we utilize data hiding techniques and absolute moment block truncation coding (AMBTC) compression to introduce an IoT-driven electronic health protection mechanism. Our mechanism employs diverse methods to compress and embed data across various image blocks. Additionally, it offers a flexible adaptation to meet different application requirements concerning embedding capacity, visual quality, and file size by adjusting thresholds and variants. Compared to alternative methods, our approach delivers superior payload capacity and efficiency while preserving visual fidelity.

IEEE Internet of Things JournalVol. 12(3)
Feng Chia University (TW), National Chin-Yi University of Technology (TW)
National Science and Technology Council
Openalex Percentile: Top 13%
Brain Tumor Detection and Classification
1.98
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