{"id":14244,"date":"2023-11-13T14:21:35","date_gmt":"2023-11-13T13:21:35","guid":{"rendered":"https:\/\/sano.science\/?post_type=people&p=14244"},"modified":"2026-03-24T11:37:46","modified_gmt":"2026-03-24T10:37:46","slug":"mahmoud-nasr","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/mahmoud-nasr\/","title":{"rendered":"Mahmoud Nasr"},"excerpt":{"rendered":"

PhD Student in Computational Neuroscience<\/p>\n","protected":false},"featured_media":20207,"template":"","people_teams":[19,33],"class_list":["post-14244","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"\nMahmoud Nasr - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mahmoud Nasr\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Computational Neuroscience\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-24T10:37:46+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/11\/Mahmoud_Nasr_Sano.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1500\" \/>\n\t<meta property=\"og:image:height\" content=\"1500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/mahmoud-nasr\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/mahmoud-nasr\\\/\",\"name\":\"Mahmoud Nasr - 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His work is dedicated to advancing state-of-the-art image enhancement and analysis methods that improve the diagnostic value of clinical imaging modalities. By developing innovative algorithms grounded in signal processing theory and quaternion-based mathematical modeling, he aims to extract meaningful structural and statistical information from complex biomedical data.<br \/>\nHis research interests extend to generative modeling and privacy-preserving computational frameworks, reflecting a commitment to both technological innovation and data security in medical applications. Mahmoud\u2019s contributions focus on creating robust, interpretable, and clinically applicable imaging solutions that bridge the gap between theoretical algorithm design and real-world healthcare deployment. Through this work, he seeks to support clinicians and researchers with reliable computational tools that enhance decision-making, improve image-driven diagnosis, and promote secure biomedical data utilization.<\/p>\n","email":"","social_media":[{"icon":{"ID":12178,"id":12178,"title":"semantic scholar","filename":"semantic-scholar.svg","filesize":7035,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/semantic-scholar.svg","link":"https:\/\/sano.science\/people\/irena-roterman-konieczna\/semantic-scholar\/","alt":"","author":"5","description":"","caption":"","name":"semantic-scholar","status":"inherit","uploaded_to":12176,"date":"2023-07-07 10:22:58","modified":"2023-07-07 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11:24:13","modified":"2023-07-06 11:24:13","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/www.linkedin.com\/in\/mahmoud-kamal-685378127\/","name":"LinkedIn"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"PhD Student in","link_text":"Computational Neuroscience","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computational-neuroscience\/"},"publications":[{"ID":23068,"post_author":"8","post_date":"2025-04-15 11:22:27","post_date_gmt":"2025-04-15 09:22:27","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-u8160B\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This publication presents a novel approach aimed at improving biometric protection in Internet of Things (IoT) environments. The method focuses on producing cancelable biometric templates that prioritize both security and privacy. By utilizing Empirical Mode Decomposition (EMD) to break down biometric signals into fundamental components and encoding these using quaternion-based techniques, the system generates templates that cannot be reversed and are exceptionally resistant to spoofing attempts.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-LQqDxP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-6Ew2yX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The approach delivers impressive performance, achieving an Area Under the Curve (AUC) of 0.9997 and an Equal Error Rate approaching zero, all while maintaining low computational requirements. This makes the solution especially appropriate for IoT devices where processing capabilities are often limited. The findings highlight the method\u2019s strong potential to tackle key security concerns in biometric applications within IoT networks.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-LQqDxP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-PkclPs\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This research exemplifies Sano\u2019s dedication to developing secure, resource-efficient, and privacy-conscious technologies at the crossroads of biometrics, artificial intelligence, and computational neuroscience.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-LQqDxP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-13Uwyq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\">Mahmoud Nasr<\/a>,\u00a0Krzysztof Brzostowski,\u00a0 Adam Pi\u00f3rkowski, Fathi E. Abd El-Samie\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-13Uwyq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI:<\/strong> 10.1038\/s41598-025-89491-2<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ANPqOt\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Quaternion Mathematics, ComputationalSecurity, EMD, IoT<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-LQqDxP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67fe2626882f9\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s41598-025-89491-2\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-LQqDxP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":23059,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-JuTUhV\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/04\/proposed-biometric-template-generation-process-1024x802.webp\" alt=\"\" class=\"wp-image-23059\"\/><figcaption class=\"wp-element-caption\">Illustration of the proposed biometric template generation process.<\/figcaption><\/figure>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-smZpQ7\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Image Source: <a href=\"https:\/\/www.nature.com\/articles\/s41598-025-89491-2\">www.nature.com\/articles<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YQdL7h\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT security","post_excerpt":"article in journal: scientific reports \u2013 the publisher Springer Nature, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"cancelable-biometric-authentication-leveraging-empirical-mode-decomposition-and-quaternion-representations-for-iot-security","to_ping":"","pinged":"","post_modified":"2025-04-15 15:43:35","post_modified_gmt":"2025-04-15 13:43:35","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=23068","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":29395,"post_author":"8","post_date":"2026-02-25 14:10:20","post_date_gmt":"2026-02-25 13:10:20","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-72jOjF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Computed tomography (CT) plays a vital role in medical diagnostics, where image quality hinges on the choice of reconstruction kernels. Sharp kernels boost spatial resolution but amplify noise, whereas soft kernels reduce noise yet blur edges. This study presents a novel Variational Mode Decomposition combined with Quaternion Bilateral Filtering (VMD-QBF) approach to transform sharp-kernel CT images into soft-kernel equivalents without losing key structural details. The method is benchmarked against standard denoising tools\u2014including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF)\u2014across multiple kernels (B50, B46, B41, B36, B35, B31). Performance is measured using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Results show VMD-QBF outperforming conventional methods, with the lowest MSE, highest PSNR, and superior structural fidelity across all kernels. These outcomes confirm the method\u2019s effectiveness in noise suppression while preserving vital image features, making it a promising option for enhancing post-reconstruction CT images.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-8ZIZmh\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_699ef52084576\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-032-09321-9_19?utm_source=researchgate.net\\u0026utm_medium=article\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:acf\/button {\"id\":\"block_699ef46b84575\",\"name\":\"acf\/button\",\"data\":{\"title\":\"\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-GiYwXe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors:<\/strong>\u00a0<a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Adam Pi\u00f3rkowski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Xq2ef2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Image Quality Metrics, Computed Tomography (CT) Imaging, Reconstruction Kernels, Image Denoising, Variational Mode Decomposition (VMD), Quaternion Bilateral Filtering (QBF), Noise Reduction<\/p>\n<!-- \/wp:paragraph -->","post_title":"Enhanced CT Image Reconstruction Using VMD-Based Quaternion Bilateral Filtering","post_excerpt":"Conference manuscript in Springer Nature Link, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"enhanced-ct-image-reconstruction-using-vmd-based-quaternion-bilateral-filtering","to_ping":"","pinged":"","post_modified":"2026-02-25 14:13:32","post_modified_gmt":"2026-02-25 13:13:32","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=29395","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":29406,"post_author":"8","post_date":"2026-02-25 14:14:56","post_date_gmt":"2026-02-25 13:14:56","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-3lKxWP\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Denoising medical images is essential for improving diagnostic precision in CT and MRI scans. This paper introduces a flexible hybrid framework that integrates multiscale decomposition methods (Empirical Mode Decomposition, Variational Mode Decomposition, Bidimensional EMD, and Multivariate EMD) with curvelet transform thresholding and conventional spatial filters. The approach was evaluated on a phantom dataset with controlled Rician noise, clinical CT images reconstructed using sharp (B50f) and medium (B46f) kernels, and MRI data acquired at different GRAPPA acceleration levels.<br>In phantom experiments, MEMD\u2013Curvelet achieved the top SSIM (0.964) and PSNR (28.35 dB), alongside strong perceptual quality (NIQE ~7.55, BRISQUE ~38.8). For CT images, VMD\u2013Curvelet and MEMD\u2013Curvelet surpassed traditional filters, delivering SSIM >0.95 and PSNR >28 dB, particularly with sharp kernels. In MRI data, MEMD\u2013Curvelet and BEMD\u2013Curvelet minimized perceptual artifacts, cutting NIQE by up to 15% and BRISQUE by 20% versus Gaussian and median filters.<br>Comparisons with deep learning methods confirmed the framework\u2019s strength: BM3D offered high fidelity but took 6.65 s per slice, while DnCNN matched SSIM (0.958) in just 2.33 s. Overall, the framework demonstrates superior noise removal and detail retention across imaging modalities, outperforming standalone filters or pure transform techniques. Its adaptability and speed highlight its value for clinical use in resource-constrained imaging scenarios.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-c5mBTS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_699ef5c68c52d\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1335\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-c5mBTS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YkTDXj\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Rafa\u0142 Obuchowicz, Adam Pi\u00f3rkowski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YkTDXj\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:  medical image denoising; curvelet transform; EMD; VMD; BEMD; MEMD; BM3D; DnCNN; CT kernels; MRI GRAPPA; rician noise; image quality metrics<\/p>\n<!-- \/wp:paragraph -->","post_title":"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques","post_excerpt":"Published in MDPI, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques","to_ping":"","pinged":"","post_modified":"2026-02-25 14:20:11","post_modified_gmt":"2026-02-25 13:20:11","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=29406","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":29360,"post_author":"8","post_date":"2026-02-24 15:14:29","post_date_gmt":"2026-02-24 14:14:29","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-UACrSF\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Medical images, particularly CT scans, often suffer from noise due to data acquisition processes, raw data handling, and hardware limitations. Accurately identifying the dominant noise type in CT images is crucial for selecting targeted denoising techniques and ensuring reliable analysis in clinical settings. This study introduces an integrated framework for noise classification using three complementary methods. The first leverages histogram similarity metrics\u2014including correlation, KL divergence, JS divergence, KS distance, and Bhattacharyya distance\u2014to statistically profile noise properties. The second relies on L2 norm and structural similarity index (SSIM) to compare noisy images against reference noise templates for precise identification. The third employs deep learning with ResNet50 to automatically detect and categorize noise patterns from raw image data. Experiments reveal Poisson noise as the primary type across evaluated CT datasets, especially in sharp- and medium-kernel reconstructions. All three methods consistently validate this, with minor instances of Rician noise detected occasionally. Other noise varieties, such as Gaussian, speckle, or salt-and-pepper, proved negligible based on SSIM values and overall similarity scores. These results highlight the framework\u2019s robustness in pinpointing noise characteristics, guiding effective denoising strategies, and enhancing medical image quality.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-QYFEk9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_699db19e04993\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1051200425002647?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-QYFEk9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-AZiTBq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud Nasr<\/a>, Krzysztof Brzostowski, Rafa\u0142 Obuchowicz, Fathi E. Abd El-Samie, Adam Pi\u00f3rkowski<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-AZiTBq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Image noise, Image processing, Correlation Coefficient, Kullback-Leibler (KL) Divergence, Jensen-Shannon (JS) Divergence, Kolmogorov-Smirnov (KS) Distance, Bhattacharyya Distance, SSIM, L<sub>2<\/sub> norm analysis, Combined metric analysis, CNN, ResNet50<\/p>\n<!-- \/wp:paragraph -->","post_title":"Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2\u00a0norm, SSIM, and CNN-based classification with ResNet50","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50","to_ping":"","pinged":"","post_modified":"2026-02-24 15:16:17","post_modified_gmt":"2026-02-24 14:16:17","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=29360","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":29370,"post_author":"8","post_date":"2026-02-24 16:14:20","post_date_gmt":"2026-02-24 15:14:20","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-m12gky\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Removing noise from reconstructed Computed Tomography (CT) images without raw projection data poses a major challenge in medical imaging, especially with sharp or medium reconstruction kernels that introduce high-frequency noise. This paper presents a novel technique combining quaternion mathematics and bilateral filtering to address this. The proposed Quaternion Bilateral Filter (QBF) preserves vital anatomical structures while reducing kernel-induced noise by representing CT scans in quaternion format, jointly encoding RGB channels. Unlike traditional methods relying on raw data or grayscale filtering, our approach operates directly on sharp-kernel reconstructed images, transforming them to match the quality of soft-kernel outputs (e.g., B30f) using paired patient data. QBF\u2019s performance is validated through full-reference metrics (SSIM, PSNR, MAE, RMSE) and no-reference perceptual measures (NIQE, BRISQUE, PIQE). Results demonstrate QBF\u2019s superiority over conventional Bilateral Filter (BF), Non-Local Means (NLM), wavelet, and CNN-based methods, with SSIM of 0.96 and PSNR of 36.3 on B50f reconstructions. Segmentation-based visual assessment confirms that QBF-preserved images retain essential structural details for downstream diagnostics. The study highlights quaternion filtering as a lightweight, interpretable, and efficient alternative to deep learning for post-reconstruction CT enhancement.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-gBB12B\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_699dbfd73a1a4\",\"name\":\"acf\/button\",\"data\":{\"title\":\"\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-gBB12B\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-8A9p1a\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud\u00a0Nasr<\/a>,\u00a0Adam\u00a0Pi\u00f3rkowski,\u00a0Krzysztof\u00a0Brzostowski,\u00a0Fathi E. Abd\u00a0El-Samie\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-8A9p1a\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Quaternion, Bilateral filter, Reconstruction kernels, Denoising, SSIM, PSNR, Image quality metrics<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-zf4hLa\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"A novel approach for CT image smoothing: Quaternion Bilateral Filtering for kernel conversion","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"29370","to_ping":"","pinged":"","post_modified":"2026-02-24 16:15:05","post_modified_gmt":"2026-02-24 15:15:05","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=29370","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":23090,"post_author":"8","post_date":"2025-04-15 11:41:05","post_date_gmt":"2025-04-15 09:41:05","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-xx6Had\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Biometric technologies are now a cornerstone in the evolving landscape of security systems. However, persistent threats such as hacking and the potential exposure of biometric data during cyberattacks highlight the pressing need for secure and innovative authentication mechanisms. One promising direction is the use of cancelable biometrics, which offer an added layer of protection for biometric templates.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"className\":\"\",\"epAnimationGeneratedClass\":\"edplus_anim-Q6oReD\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">In this study, we introduce a novel variant of cancelable biometrics that leverages the robust capabilities of the Double Random Phase Encoding (DRPE) algorithm based on <strong>Quaternion Mathematics<\/strong>. By integrating an additional biometric input and utilizing quaternion-based computations, our method achieves outstanding results that go beyond existing benchmarks.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-xx6Had\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The cancelable face recognition framework we propose maintains impressive reliability under noisy conditions, as confirmed by simulation tests. Specifically, our system achieves an area under the ROC curve of 0.994 and an exceptionally low Equal Error Rate (EER) of just 0.00174. Furthermore, it exhibits remarkable efficiency, generating each secure template in approximately 0.056263 seconds. These results underscore the solution\u2019s high performance and suitability for tackling modern security challenges.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-cK4KZ0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-A0TAeQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\">Mahmoud Nasr<\/a>,  Adam Pi\u00f3rkowski, Fathi E. Abd El-Samie <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-CI1ZD8\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1007\/s11042-024-18621-1<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Ahox0y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Quaternion Mathematics<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-cK4KZ0\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67fe289f7c39f\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18621-1\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Quaternion double random phase encoding for privacy-preserving cancelable biometrics","post_excerpt":"Journal paper in: Springer Nature, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"quaternion-double-random-phase-encoding-for-privacy-preserving-cancelable-biometrics","to_ping":"","pinged":"","post_modified":"2025-04-15 15:22:40","post_modified_gmt":"2025-04-15 13:22:40","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=23090","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/14244","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":19,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/14244\/revisions"}],"predecessor-version":[{"id":30089,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/14244\/revisions\/30089"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/20207"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=14244"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=14244"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}