{"id":29406,"date":"2026-02-25T14:14:56","date_gmt":"2026-02-25T13:14:56","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=29406"},"modified":"2026-02-25T14:20:11","modified_gmt":"2026-02-25T13:20:11","slug":"denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/denoising-of-ct-and-mri-images-using-decomposition-based-curvelet-thresholding-and-classical-filtering-techniques\/","title":{"rendered":"Denoising of CT and MRI Images Using Decomposition-Based Curvelet Thresholding and Classical Filtering Techniques"},"content":{"rendered":"\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 &gt;0.95 and PSNR &gt;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\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.mdpi.com\/2076-3417\/16\/3\/1335\" target=\"_self\"  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>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\n\n\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp; medical image denoising;&nbsp;curvelet transform; EMD; VMD; BEMD; MEMD; BM3D; DnCNN; CT kernels; MRI GRAPPA;&nbsp;rician noise;&nbsp;image quality metrics<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Published in MDPI, 2025<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-29406","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) - 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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 &gt;0.95 and PSNR &gt;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","innerContent":["\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 &gt;0.95 and PSNR &gt;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"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-c5mBTS","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.mdpi.com\/2076-3417\/16\/3\/1335","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-c5mBTS","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-YkTDXj","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\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","innerContent":["\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"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-YkTDXj","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp; medical image denoising;&nbsp;curvelet transform; EMD; VMD; BEMD; MEMD; BM3D; DnCNN; CT kernels; MRI GRAPPA;&nbsp;rician noise;&nbsp;image quality metrics<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>:&nbsp; medical image denoising;&nbsp;curvelet transform; EMD; VMD; BEMD; MEMD; BM3D; DnCNN; CT kernels; MRI GRAPPA;&nbsp;rician noise;&nbsp;image quality metrics<\/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\/29406","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":7,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29406\/revisions"}],"predecessor-version":[{"id":29415,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/29406\/revisions\/29415"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=29406"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=29406"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=29406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}