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.

With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to the ability of sharing secret information between two receivers and the larger embedding capacity compared to the traditional reversible data hiding scheme, dual digital images have also attracted extensive research in the past decade. In this paper, we propose a reversible data hiding scheme based on matrix coding using dual digital images. By modifying the bits in the pixels, we can conceal three bits of the secret message in two pixels. In other words, the embedding rate reaches 1.5 bits per pixel (bpp). The experimental results demonstrate that our method has a significantly larger embedding capacity of 786,432 bits compared to previous similar methods while still maintaining acceptable image quality defined by a peak signal-to-noise ratio (PSNR) greater than 30 dB. The proposed scheme is suitable for applications required to pass a large amount of data but with minor security of image quality to be visually acceptable.

To demonstrate that rifabutin-related corneal deposits are localized to the deep stroma using anterior segment optical coherence tomography (OCT) and confocal microscopy. A 55-year-old male with a history of human immunodeficiency virus (HIV) and disseminated mycobacterium avium complex on rifabutin treatment for 3 years presented with bilateral corneal deposits. Confocal microscopy and anterior segment OCT confirm that rifabutin-related corneal deposits are located in the deep stroma, rather than in the endothelium. And Importance: Rifabutin deposits localize to the deep corneal stroma, and can be seen with both confocal microscopy and anterior segment OCT. Anterior segment OCT is a widely available and easily used diagnostic tool, and can provide utility in the diagnosis of corneal deposits.

In recent decades, information security has become increasingly valued, including many aspects of privacy protection, copyright protection, and digital forensics. Therefore, many data hiding schemes have been proposed and applied to various carriers such as text, images, audio, and videos. Vector Quantization (VQ) compression is a well-known method for compressing images. In previous research, most methods related to VQ compressed images have focused on hiding information in index tables, while only a few of the latest studies have explored embedding data in codebooks. We propose a data hiding scheme for VQ codebooks. With our approach, a sender XORs most of the pixel values in a codebook and then applies a threshold to control data embedding. The auxiliary information generated during this process is embedded alongside secret data in the index reordering phase. Upon receiving the stego codebook and the reordered index table, the recipient can extract the data and reconstruct the VQ-compressed image using the reverse process. Experimental results demonstrate that our scheme significantly improves embedding capacity compared to the most recent codebook-based methods. Specifically, we observe an improvement rate of 223.66% in a small codebook of size 64 and an improvement rate of 85.19% in a codebook of size 1024.