{"id":29360,"date":"2026-02-24T15:14:29","date_gmt":"2026-02-24T14:14:29","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=29360"},"modified":"2026-02-24T15:16:17","modified_gmt":"2026-02-24T14:16:17","slug":"comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50\/","title":{"rendered":"Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2\u00a0norm, SSIM, and CNN-based classification with ResNet50"},"content":{"rendered":"\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\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1051200425002647?via%3Dihub\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tRead the article\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud&nbsp;Nasr<\/a>,&nbsp;Krzysztof&nbsp;Brzostowski,&nbsp;Rafa\u0142&nbsp;Obuchowicz,&nbsp;Fathi E.&nbsp;Abd El-Samie,&nbsp;Adam&nbsp;Pi\u00f3rkowski<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp;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>&nbsp;norm analysis, Combined metric analysis, CNN, ResNet50<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-29360","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2\u00a0norm, SSIM, and CNN-based classification with ResNet50 - 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\/research\/comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2\u00a0norm, SSIM, and CNN-based classification with ResNet50\" \/>\n<meta property=\"og:description\" content=\"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 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50\/\" \/>\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=\"2026-02-24T14:16:17+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/comparative-analysis-of-noise-estimation-methods-in-computed-tomography-images-histogram-analysis-l2-norm-ssim-and-cnn-based-classification-with-resnet50\\\/\",\"name\":\"Comparative analysis of noise estimation methods in computed tomography images: Histogram analysis, L2\u00a0norm, SSIM, and CNN-based classification with ResNet50 - 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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","innerContent":["\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"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-QYFEk9","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"Read the article","button_type":"link","url":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1051200425002647?via%3Dihub","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-QYFEk9","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-AZiTBq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud&nbsp;Nasr<\/a>,&nbsp;Krzysztof&nbsp;Brzostowski,&nbsp;Rafa\u0142&nbsp;Obuchowicz,&nbsp;Fathi E.&nbsp;Abd El-Samie,&nbsp;Adam&nbsp;Pi\u00f3rkowski<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/mahmoud-nasr\/\" type=\"people\" id=\"14244\">Mahmoud&nbsp;Nasr<\/a>,&nbsp;Krzysztof&nbsp;Brzostowski,&nbsp;Rafa\u0142&nbsp;Obuchowicz,&nbsp;Fathi E.&nbsp;Abd El-Samie,&nbsp;Adam&nbsp;Pi\u00f3rkowski<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-AZiTBq","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp;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>&nbsp;norm analysis, Combined metric analysis, CNN, ResNet50<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp;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>&nbsp;norm analysis, Combined metric analysis, CNN, ResNet50<\/p>\n"]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29360","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":8,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29360\/revisions"}],"predecessor-version":[{"id":29369,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29360\/revisions\/29369"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=29360"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=29360"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=29360"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}