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High-Payload and Secure Data Hiding for Medical Images in IoMT-Based eHealth Systems

Year: 2026

Type: article

Abstract: With the rapid advancement of the Internet of Medical Things (IoMT), the efficient transmission and management of large-scale medical images in bandwidth- and resource-constrained networks remain critical challenges. This paper proposes a high-payload data hiding method in Absolute Moment Block Truncation Coding (AMBTC)-compressed medical images based on block classification. Image blocks are categorized into flat, smooth, and complex types according to the difference between high and low values, and adaptive embedding and extraction strategies are applied to each type. The proposed method integrates secret data into the compression framework, thereby enhancing efficiency while maintaining visual quality. Experimental results demonstrate an average efficiency of 59% and an average PSNR of approximately 30 dB. Furthermore, visual and structural evaluations indicate that the proposed method effectively preserves textures and boundaries. These results confirm the feasibility of integrating high-payload data hiding into AMBTC compression for efficient medical image storage and transmission in IoMT environments.

Source: Sensors

DOI: 10.3390/s26103032

Authors: Yichen Wang, Yijie Lin, Ching-Chun Chang, Ching-Chun Chang, Chin-Chen Chang, Chin-Chen Chang, Wu-Yuin Hwang

Institutions: Shandong Institute of Business and Technology, Feng Chia University, National Institute of Informatics, National Central University, National Dong Hwa University

Language: English

Topic: Advanced Steganography and Watermarking Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

OpenAlex ID: W7160859961

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Cites: 27

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