Xue Xingsi, Marappan Raja, Raju Sekar Kidambi, Raghavan Rangarajan, Rajan Rengasri, Khalaf Osamah Ibrahim, Abdulsahib Ghaida Muttashar
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350011, China.
School of Computing, SASTRA Deemed University, Thanjavur 613401, India.
Bioengineering (Basel). 2023 Mar 6;10(3):333. doi: 10.3390/bioengineering10030333.
Due to rapidly developing technology and new research innovations, privacy and data preservation are paramount, especially in the healthcare industry. At the same time, the storage of large volumes of data in medical records should be minimized. Recently, several types of research on lossless medically significant data compression and various steganography methods have been conducted. This research develops a hybrid approach with advanced steganography, wavelet transform (WT), and lossless compression to ensure privacy and storage. This research focuses on preserving patient data through enhanced security and optimized storage of large data images that allow a pharmacologist to store twice as much information in the same storage space in an extensive data repository. Safe storage, fast image service, and minimum computing power are the main objectives of this research. This work uses a fast and smooth knight tour (KT) algorithm to embed patient data into medical images and a discrete WT (DWT) to protect shield images. In addition, lossless packet compression is used to minimize memory footprints and maximize memory efficiency. JPEG formats' compression ratio percentages are slightly higher than those of PNG formats. When image size increases, that is, for high-resolution images, the compression ratio lies between 7% and 7.5%, and the compression percentage lies between 30% and 37%. The proposed model increases the expected compression ratio and percentage compared to other models. The average compression ratio lies between 7.8% and 8.6%, and the expected compression ratio lies between 35% and 60%. Compared to state-of-the-art methods, this research results in greater data security without compromising image quality. Reducing images makes them easier to process and allows many images to be saved in archives.
由于技术的快速发展和新的研究创新，隐私和数据保护至关重要，尤其是在医疗行业。同时，应尽量减少医疗记录中大量数据的存储。最近，已经开展了几种关于无损医学重要数据压缩和各种隐写术方法的研究。本研究开发了一种结合先进隐写术、小波变换（WT）和无损压缩的混合方法，以确保隐私和存储。本研究专注于通过增强安全性和优化大数据图像存储来保护患者数据，使药理学家能够在大型数据存储库的相同存储空间中存储两倍的信息。安全存储、快速图像服务和最小计算能力是本研究的主要目标。这项工作使用快速平滑的骑士巡游（KT）算法将患者数据嵌入医学图像，并使用离散小波变换（DWT）来保护屏蔽图像。此外，使用无损分组压缩来最小化内存占用并最大化内存效率。JPEG格式的压缩率百分比略高于PNG格式。当图像尺寸增加时，即对于高分辨率图像，压缩率在7%至7.5%之间，压缩百分比在30%至37%之间。与其他模型相比，所提出的模型提高了预期的压缩率和百分比。平均压缩率在7.8%至8.6%之间，预期压缩率在35%至60%之间。与现有技术方法相比，本研究在不影响图像质量的情况下实现了更高的数据安全性。减少图像使其更易于处理，并允许在存档中保存许多图像。
