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.

An adaptive feature extraction method for classification of Covid-19 X-ray images.

This study aims to detect Covid-19 disease in the fastest and most accurate way from X-ray images by developing a new feature extraction method and deep learning model . Partitioned Tridiagonal Enhanced Multivariance Products Representation (PTMEMPR) method is proposed as a new feature extraction method by using matrix partition in TMEMPR method which is known as matrix decomposition method in the literature. The proposed method which provides 99.9% data reduction is used as a preprocessing method in the scheme of the Covid-19 diagnosis. To evaluate the performance of the proposed method, it is compared with the state-of-the-art feature extraction methods which are Singular Value Decomposition(SVD), Discrete Wavelet Transform(DWT) and Discrete Cosine Transform(DCT). Also new deep learning models which are called FSMCov, FSMCov-N and FSMCov-L are developed in this study. The experimental results indicate that the combination of newly proposed feature extraction method and deep learning models yield an overall accuracy 99.8%.

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.)

Reconstruction of Time-varying Graph Signals via Sobolev Smoothness

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals. IEEE

Classification of Covid-19 X-ray Images Using Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR)

Medical images are crucial data sources for diseases that can not be diagnosed easily. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as difficulties in data storage, computational load, and the time required to process high-dimensional data. It is vital to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration. Disease diagnosis from these images can be summarized in two steps as preprocessing and classification. The preprocessing step covers the feature extraction process and for this the recently developed decomposition-based method, Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR), is proposed as a feature extraction method. The classification of images is the second step where the methods of Random Forests and Support Vector Machines are applied. Also, the X-ray images have been reduced by 99,9% with TMEMPR and with several state-of-the-art feature extraction methods such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT). The results are examined with regard to different feature extraction methods and it is observed that a higher accuracy rate is achieved when the TMEMPR method is used. © 2021 IEEE

A recommender system approach for predicting effective antivirals

Emerging infectious diseases such as COVID-19, caused by the SARS-CoV-2 virus, require systematic strategies to assist in the discovery of effective treatments. Drug repositioning, the process of finding new therapeutic indications for commercialized drugs, is a promising alternative to the development of new drugs, with lower costs and shorter development times. In this paper, we propose a recommendation system called geometric confidence non-negative matrix factorization (GcNMF) to assist in the repositioning of 126 broad spectrum antiviral drugs for 80 viruses, including SARS-CoV-2. GcNMF models the non-Euclidean structure of the space using graphs, and produces a ranked list of drugs for each virus. Our experiments reveal that GcNMF significanlty outperforms other matrix decomposition methods at predicting missing drug-virus associations. Our analysis suggests that GcNMF could assist pharmacological experts in the search for effective drugs against viral diseases. ©2021 IEEE

WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition.

The low capture rate of expressed RNAs from single-cell sequencing technology is one of the major obstacles to downstream functional genomics analyses. Recently, a number of imputation methods have emerged for single-cell transcriptome data, however, recovering missing values in very sparse expression matrices remains a substantial challenge. Here, we propose a new algorithm, WEDGE (WEighted Decomposition of Gene Expression), to impute gene expression matrices by using a biased low-rank matrix decomposition method. WEDGE successfully recovered expression matrices, reproduced the cell-wise and gene-wise correlations and improved the clustering of cells, performing impressively for applications with sparse datasets. Overall, this study shows a potent approach for imputing sparse expression matrix data, and our WEDGE algorithm should help many researchers to more profitably explore the biological meanings embedded in their single-cell RNA sequencing datasets. The source code of WEDGE has been released at https://github.com/QuKunLab/WEDGE.