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

Singh, Kamred Udham; Hsieh, Sun-Yuan; Swarup, Chetan; Singh, Teekam

doi:10.18280/ts.390127

Cited by 5 publications

(4 citation statements)

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“…The semi-supervised training approach suggested in the article was contrasted with the supervised training, with different features and feature combinations [29,30]. As shown in Table 5, the proposed approach met all three criteria given here.…”

Section: One Sample Testmentioning

confidence: 95%

AIAVRT: 5.0 Transformation in Medical Education with Next Generation AI- 3D Animation and VR Integrated Computer Graphics Imagery

Kumar¹,

Saudagar²,

Alkhathami³

et al. 2022

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Immersive recreations of actual classrooms in virtual reality (VR) provide students and teachers with insights into the classroom. Recent technological advances in haptics, display systems, and motion detection create a realistic and interactive experience, making VR ideal for hands-on training. The main fields of application for VR are interventional procedures like surgeries. VR devices allow teachers to quickly identify and address difficulties in the clever learning for complicated surgery course. With this in mind, it stands to reason that students will do better in logical thinking in a virtual classroom. Augmented reality (AR) enables users to project virtual information and structures over physical objects, enhancing or changing the real world. It is very beneficial to incorporate AR applications in understanding anatomical structures and physiological mechanisms. Many scholars have attempted to demonstrate the validity and educational impact of various VR and AR applications on different hardware platforms. Some even proposed a curriculum that incorporates these methods. This article aims to assist universities in maintaining and improving their education and learning systems through the use of a VR-based e-learning platform. Machine learning (ML) and artificial intelligence (AI) were employed to assess the learning impact of VR in medical education. In addition, the authors predicted student performance through multiple validation tests, such as the T-Test, P-Test, and One-Sample Validation test, and analyzed the proposed model with different parameters.

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Section: One Sample Testmentioning

confidence: 95%

AIAVRT: 5.0 Transformation in Medical Education with Next Generation AI- 3D Animation and VR Integrated Computer Graphics Imagery

Kumar¹,

Saudagar²,

Alkhathami³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This alters the original placements of the pixels in the image at random; we use it for watermark encryption. Because of its simplicity, periodicity, and reversibility, this 2D mapping approach is used in encryption and watermarking [36]. A transform's periodicity and reversibility suggest that the original data will be recovered after a fixed iteration if applied repeatedly to a given matrix.…”

Section: Arnold Cat Mapmentioning

confidence: 99%

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

et al. 2023

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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.

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“…NIfTI now supports data types that the Analyze format did not previously support, such as unsigned 16-bit. Image files are typically saved as a single ".nii" file that contains both the header and the pixel data [22]. NIfTI file format is represented as I×J×N, where I×J represent the slice dimension, and the N represents the number of slices in the NIfTI file.…”

Section: Background Theoriesmentioning

confidence: 99%

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

et al. 2022

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Telemedicine has been intensely promoted in the present pandemic situation of COVID-19 to maintain a strategic distance from the infected person. Several medical tests were used to detect the coronavirus, including antigen, RT-PCR, and a lung CT scan. Only a lung CT-Scan can detect the coronavirus and provide information about the lung infection. As a result, digital imaging plays a critical role in the current pandemic situation. Teleradiology allows for the communication of digital medical images of patients over the internet for diagnosis. A lung CT-Scan test is currently being performed on billions of people to detect COVID-19. These images were sent via the internet for diagnosis and research purposes. The NIfTI image file (.nii extension) was created by the CT-Scanner and contains multiple slices of the lungs. As a result, radiologists determine that the received image has not been tempered during transmission, posing a critical authenticity problem when transmitting these images over the internet. As a result, the researchers are more concerned about the integrity and authenticity of these images in teleradiology. This paper proposes a blind, robust watermarking scheme for lung CT-Scan NIfTI images to address this issue. We use Otsu's image segmentation algorithm in the proposed scheme to identify the slice with the least amount of medical information for watermark embedding. The proposed scheme employs the Discrete Shearlet Transform (DST), Lifting Wavelet Transform (LWT), and Schur decomposition to embed the encrypted watermark. Watermarks are encrypted using the Affine Transform. The experimental results show that watermarked slice has been tainted by the addition of various sorts of noise, including salt-and-pepper noise, compression, Gaussian noise, speckle noise, and motion blur. After an attack, a watermark is retrieved, and the NC values of extracted watermarks are 0.99623 for Salt and pepper noise, 0.96964 for Gaussian noise, 0.99014 for Speckle noise. The proposed scheme was put to the test with a variety of attacks and produced significant results.

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

Cited by 5 publications

References 0 publications

AIAVRT: 5.0 Transformation in Medical Education with Next Generation AI- 3D Animation and VR Integrated Computer Graphics Imagery

AIAVRT: 5.0 Transformation in Medical Education with Next Generation AI- 3D Animation and VR Integrated Computer Graphics Imagery

Secure NIfTI Image Authentication Scheme for Modern Healthcare System

A Robust NIfTI Image Authentication Framework Based on DST and Multi-Scale Otsu Thresholding

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