With the progression of picture archiving and communication systems (PACSs) over the past decade, it has become imperative that such systems be optimised in security, storage, and transmission aspects. The work presented in this Letter shows a framework for medical image compression and secure image transmission for PACSs. The work aims to achieve a lower dimensionality of input medical image signified by a high-compression ratio, a secure image transmission that can withstand adversarial attacks and provide a reversible reconstruction with minimal error. The authors illustrate that sinusoid modulated Gaussian texture maps, multi-level chaotic maps, and high-frequency image maps can be efficiently fused and utilised in a deep learning architecture. The overall analysis depicts promising results with regard to the capability of image compression, security, and transmission. The proposed framework will be a potential candidate for use in PACSs, which effectively is the backbone of the current healthcare paradigm.

中文翻译：

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为医疗保健中的图片存档和通信系统实现可逆的低维转换

随着过去十年图片存档和通信系统 (PACS) 的发展，必须在安全、存储和传输方面优化此类系统。这封信中介绍的工作展示了一个用于 PACS 的医学图像压缩和安全图像传输的框架。该工作旨在实现输入医学图像的低维数，即高压缩比，一种安全的图像传输，可以抵御对抗性攻击，并以最小的错误提供可逆重建。作者说明了正弦调制高斯纹理图、多级混沌图和高频图像图可以在深度学习架构中有效融合和利用。总体分析描绘了有关图像压缩能力的有希望的结果，安全，传输。提议的框架将成为用于 PACS 的潜在候选者，它实际上是当前医疗保健范式的支柱。