Academic Journal

Authentication of Lung CT-Scan Data in NIfTI Format Using LWT, Hessenberg Decomposition, and Affine Transform.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Authentication of Lung CT-Scan Data in NIfTI Format Using LWT, Hessenberg Decomposition, and Affine Transform.
Συγγραφείς: Singh, Kamred Udham, Kumar, Ankit, Singh, Teekam
Πηγή: IETE Journal of Research; Apr2024, Vol. 70 Issue 4, p3590-3602, 13p
Θεματικοί όροι: Digital image watermarking, Technological innovations, Matrix decomposition, Digital watermarking, Image processing, Technological progress
Περίληψη: Technological advancement in digital medical imaging changes the world health care system because various diseases are diagnosed through these technologies. In the Covid-19 phase, telemedicine played a tremendous role in providing remote medical consultation in rural areas. But in remote consultation, various medical images send to a radiologist for diagnosis through the internet. Worldwide has seen a significant surge in digital media attacks that replicate and tamper with the digital image, resulting in a breach of authenticity and ownership. A robust and safe watermarking scheme for NIfTI images has been proposed in this paper. This novel method entails meticulously integrating a watermark in the slice of the NIfTI image. We aim to correctly incorporate the watermark with minimal distortion and retain the medical information of the selected image slice. The proposed method uses LWT transform to transform the image, allowing for surprisingly good modification during insertion. Furthermore, Hessenberg matrix decomposition is applied on the LL sab bands with the image's maximal energy to be retained. Scrambling the watermark before embedding it in the slice is accomplished using the Affine transform. A thorough study of the trade-off between security, imperceptibility, and robustness utilizing performance measures viz. NC, PSNR, SNR, and SSIM have been given. The simulation findings have been validated against image processing threats. [ABSTRACT FROM AUTHOR]
Copyright of IETE Journal of Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
Περιγραφή
ISSN:03772063
DOI:10.1080/03772063.2023.2195372