In recent years, virtual reality has experienced steady growth in the medical field, such as surgery, rehabilitation, disease diagnostic, and learning. The 3D representation of radiological images plays a significant role in disease diagnostic and treatment planning compared to standard 2D medical images. Since March 2019, almost all laboratories and medical centers have improved their patients' management methods with confirmed coronavirus (COVID-19) disease. Providing appropriate treatment in the well moment may contribute to save lives. Our study aims to develop an advanced COVID-19 CT scan image segmentation and 3D visualization using an unsupervised thresholding procedure and virtual reality technology to better plan and monitor affected patients. Our proposed system provides three-dimensional COVID-19 lesion visualization, which clearly shows segmented infected region (in 3D) rather than traditional two-dimensional images.
Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.
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