Dynamic Colormap Visualization Integratedwith Harris Hawks Optimization for EnhancedLung CT Segmentation and Diagnostic Precision

Dynamic Colormap Visualization Integratedwith Harris Hawks Optimization for EnhancedLung CT Segmentation and Diagnostic Precision | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic Colormap Visualization Integratedwith Harris Hawks Optimization for EnhancedLung CT Segmentation and Diagnostic Precision Osama Dorgham, Mohammad Ryalat, Nijad Najdawi, Rami Alkhawaldeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5314417/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study presents a novel method that utilizes Harris Hawks Optimization (HHO) combined with dynamic colormap visualization to enhance the quality of lung CT scan segmentation. The Harris Hawks optimization algorithm is a swarm-based method used to enhance multi-level thresholding for image segmentation, hence facilitating the identification of regions of interest (ROIs) in medical images. An analysis of different colormap schemes, including Accent, Gray, Hot, Inferno and Jet, was conducted to improve the visualization of segmented images.The experimental results show the efficiency of the HHO algorithm from the segmentationaccuracy perspective as compared to the conventional optimization techniques using publicly available datasets from the Cancer Imaging Archive. In particular, the average SSIM was above 98% while the Jaccard Index was more than 90%. The expert evaluation confirms earlier findings that using the HHO algorithm with the Inferno colormap, particularly with four or five thresholds, achieves optimal image clarity and diagnostic value for clinical purposes. In addition, the method provides a promising way to enhance diagnostic precision and treatment strategies for lung diseases, making it highly valuable for pulmonaryhealthcare, particularly in urgent scenarios such as pandemics. Harris Hawks Optimization (HHO) Dynamic Colormap Visualization Lung CT Segmentation Multi-level Thresholding Medical Imaging Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Jan, 2025 Reviews received at journal 09 Nov, 2024 Reviews received at journal 06 Nov, 2024 Reviewers agreed at journal 01 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers invited by journal 29 Oct, 2024 Editor assigned by journal 23 Oct, 2024 Submission checks completed at journal 23 Oct, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5314417","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372884250,"identity":"d7dc97a6-cd6b-49aa-bcf6-8411bb9a51ab","order_by":0,"name":"Osama 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