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

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.