A robust NIfTI image authentication framework to ensure reliable and safe diagnosis.

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

Advances in digital neuroimaging technologies, i.e., MRI and CT scan technology, have radically changed illness diagnosis in the global healthcare system. Digital imaging technologies produce NIfTI images after scanning the patient's body. COVID-19 spared on a worldwide effort to detect the lung infection. CT scans have been performed on billions of COVID-19 patients in recent years, resulting in a massive amount of NIfTI images being produced and communicated over the internet for diagnosis. The dissemination of these medical photographs over the internet has resulted in a significant problem for the healthcare system to maintain its integrity, protect its intellectual property rights, and address other ethical considerations. Another significant issue is how radiologists recognize tempered medical images, sometimes leading to the wrong diagnosis. Thus, the healthcare system requires a robust and reliable watermarking method for these images. Several image watermarking approaches for .jpg, .dcm, .png, .bmp, and other image formats have been developed, but no substantial contribution to NIfTI images (.nii format) has been made. This research suggests a hybrid watermarking method for NIfTI images that employs Slantlet Transform (SLT), Lifting Wavelet Transform (LWT), and Arnold Cat Map. The suggested technique performed well against various attacks. Compared to earlier approaches, the results show that this method is more robust and invisible.

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

Technological advancement in digital medical imaging changes the world health care system because various diseases are diagnosed through these technologies. In the current covid-19 phase, telemedicine plays 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 or posted to a listserv without the copyright holder's express written permission. 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.)

Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain.

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones.

Image-based decision making for reliable and proper diagnosing in NIFTI format using watermarking.

Nowadays, advancement in Magnetic Resonance Imaging (MRI) and Computed Tomography Scan (CT-Scan) technologies have defined modern neuroimaging and drastically change the diagnosing of disease in the world healthcare system. These imaging technologies generate NIFTI (Neuroimaging Informatics Technology Initiative) images. Due to COVID-19 last several months CT-Scan has been performed on millions of the CORONA patients, so billions of the NIFTI images have been produced and communicate over the internet for the diagnosing purpose to detect the coronavirus. The communication of these medical images over the internet yielding the major problem of integrity, copyright protection, and other ethical issues for the world health care system. Another critical problem is that; is doctor diagnose the impeccable medical image of the patient because a large amount of COVID-19 patient's data exists. For proper diagnosing it is also necessary to identify impeccable medical image. Therefore, to address these problems a secure and robust watermarking scheme is needed for these images. Various watermarking schemes have been developed for bmp, .jpg, .png, DICOM, and other image formats but the noticeable contribution is not reported for the NIFTI images. In this paper a robust and hybrid watermarking scheme for NIFTI images based on Lifting Wavelet Transform (LWT), MSVD (Multiresolution Singular Value Decomposition) and QR factorization. The combination of LWT, QR, and MSVD helps in retaining the sensitivity of the NIFTI image and improve the robustness of the watermarking scheme. In this scheme, multiple watermarks are inserted across the first slice of the NIFTI image. The proposed watermarking scheme is sustained against various noise attacks and performance is measured in terms of PSNR, SNR, SSIM, Quality of image, and Normalized correlation. Quality of the image is much significant that lie between .99994 to .99998 and SSIM reported from .94 to .99. Whereas the PSNR of the proposed scheme lies between 56.76 to 57.28 db and NC values lie between .9993 to .9998. which shows that the results are better than the existing schemes where PSNR is lies between 32.66 to 52.02 db. Watermarking, NIFTI, MSVD, LWT, QR and Image.