Authentication of NIfTI Neuroimages Using Lifting Wavelet Transform, Arnold Cat Map, Z-Transform, and Hessenberg Decomposition

Abstract

The technological progress in digital medical imaging has enabled the diagnosis of various ailments, and thus upgraded the global healthcare system. In the era of coronavirus 2019 (COVID-19), telemedicine plays the crucial role of supporting remote medical consultation in rural locations. During the remote consultation, numerous medical images are sent to each radiologist via the Internet. There has been a surge in the number of attacks on digital medical images worldwide, which severely threatens authenticity and ownership. To mitigate the threat, this paper proposes a robust and secure watermarking approach for NIfTI images. Our approach painstakingly incorporates a watermark into the chosen NIfTI image slice, aiming to accurately fit the watermark, while preserving the medical information contained in the slice. Specifically, the original image was converted through the lifting wavelet transform (LWT), realizing excellent modification during insertion. Next, Z-transform was applied over the low-low (LL) band, and the Hessenberg decomposition (HD) was performed on the transformed band, which contains the maximum energy of the image. Afterwards, Arnold Cat map was employed to scramble the watermark, before inserting it into the slice. Simulation results show that our approach strikes a perfect balance between security, imperceptibility, and robustness against various attacks, as suggested by metrics like peak signal-to-noise ratio (PSNR), normalized correlation (NC), structural similarity index measure (SSIM), and universal image quality index Q.