Balancing Accuracy and Efficiency: A Comprehensive Analysis of Optimization Algorithms in Medical Image Segmentation

Balancing Accuracy and Efficiency: A Comprehensive Analysis of Optimization Algorithms in Medical Image Segmentation | 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 Article Balancing Accuracy and Efficiency: A Comprehensive Analysis of Optimization Algorithms in Medical Image Segmentation Nijad A. Al-Najdawi, Ali F. Al-Shawabkeh, Sara Tedmori, Ibrahim I. Ikhries, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6507057/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Medical image segmentation algorithms play a crucial role in assisting healthcare professionals with disease identification, research, and diagnosis. Numerous digital image segmentation methods have been developed, with multilevel thresholding techniques consistently outperforming others in terms of evaluation metrics. The standard algorithms include classical statistical methods, such as the Otsu and Kapur methods, which yield highly accurate results. However, when applied to multilevel thresholding, these methods incur significant computational costs, presenting an optimization challenge. In this work, a set of well-known optimization algorithms is integrated with Otsu’s method to assess their effectiveness in reducing computational demands while preserving optimal segmentation quality. Experiments are conducted on publicly available datasets, including chest images with associated clinical and genomic data. This work evaluates the performance of each optimization algorithm in combination with Otsu's method, highlighting those that achieve substantial reductions in computational cost and convergence time while maintaining a competitive level of segmentation quality. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Medical images segmentation optimization algorithms Multi-level Thresholding computational efficiency Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Reviewers agreed at journal 19 May, 2025 Reviews received at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 15 May, 2025 Editor assigned by journal 12 May, 2025 Editor invited by journal 12 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 22 Apr, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6507057","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":458800538,"identity":"e52c5e17-db3f-4f88-9222-4b3fa65692b1","order_by":0,"name":"Nijad A. 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