Achieving a reversible lower dimensionality transformation for picture archiving and communication system in healthcare

• Published in: Electronics letters Vol. 56; no. 17; pp. 863 - 865
• Main Authors: Banday, Shoaib Amin, Pandit, Mohammad Khalid, Khan, Ab Rouf
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 20.08.2020
• Subjects: adversarial attacks; Biomedical technology; data compression; deep learning architecture; Gaussian processes; health care; high‐compression ratio; high‐frequency image maps; image coding; image texture; input medical image; learning (artificial intelligence); medical image compression; medical image processing; multilevel chaotic maps; neural net architecture; PACS; picture archiving and communication systems; reversible lower dimensionality transformation; reversible reconstruction; secure image transmission; security of data; sinusoid modulated Gaussian texture maps; transmission aspects; visual communication; data compression; deep learning architecture; PACS; reversible reconstruction; visual communication; transmission aspects; image texture; high-compression ratio; picture archiving and communication systems; input medical image; high-frequency image maps; security of data; multilevel chaotic maps; reversible lower dimensionality transformation; Gaussian processes; medical image compression; neural net architecture; adversarial attacks; learning (artificial intelligence); secure image transmission; image coding; medical image processing; health care; sinusoid modulated Gaussian texture maps
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2020.0992

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.