Highlights Automatically enhance, divide, and validate the COVID-19 CT images into regions with similar properties such as contrast and structure. Efficient Kapur entropy-based multilevel thresholding unsupervised procedure. Measure, visualize, and study comparisons of the infected by COVID-19 volume. The experiment results indicate that the proposed method can reach the desired heat-mapping of COVID-19 lesion and has the potential to be used for clinical application including developing country.

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Enhancing facial images captured under different lighting conditions is an important challenge and a crucial component in the Automatic Face Recognition Systems (AFRS). We tackle this problem by proposing a new face image enhancement approach based on Fuzzy theory. Depending on the illumination of a given image, the Fuzzy-logic generates an adaptive factor which is used for correcting the illumination. The proposed approach improves non-uniform illumination and low contrasts, often encountered during capturing process in severe environmental conditions. Our approach is assessed using four blind-reference image quality metrics as well as visual assessment. A comparison to six state-of-the-art methods is provided. Experiments are performed on four public data sets, namely EYale-B, Mobio, FERET and CMU-PIE, showing very interesting results achieved by our approach.

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Recently many studies have shown the effectiveness of using augmented reality (AR) and virtual reality (VR) in biomedical image analysis. However, they are not automating the COVID level classification process. Additionally, even with the high potential of CT scan imagery to contribute to research and clinical use of COVID-19 (including two common tasks in lung image analysis: segmentation and classification of infection regions), publicly available data-sets are still a missing part in the system care for Algerian patients. This article proposes designing an automatic VR and AR platform for the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic data analysis, classification, and visualization to address the above-mentioned challenges including (1) utilizing a novel automatic CT image segmentation and localization system to deliver critical information about the shapes and volumes of infected lungs, (2) elaborating volume measurements and lung voxel-based classification procedure, and (3) developing an AR and VR user-friendly three-dimensional interface. It also centered on developing patient questionings and medical staff qualitative feedback, which led to advances in scalability and higher levels of engagement/evaluations. The extensive computer simulations on CT image classification show a better efficiency against the state-of-the-art methods using a COVID-19 dataset of 500 Algerian patients. The developed system has been used by medical professionals for better and faster diagnosis of the disease and providing an effective treatment plan more accurately by using real-time data and patient information.

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