Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm

Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm | 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 Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm Soheil Fakheri, Mahmoud Alimoradi, Mohammad Reza Yamaghani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4479475/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Colour image segmentation is an essential task in image processing and computer vision that aims to divide an image into meaningful and homogeneous regions. One of the most widely used techniques for colour image segmentation is multilevel thresholding, which selects a set of optimal threshold values to separate the image pixels into different classes. However, finding the optimal thresholds is a complex and computationally intensive problem requiring efficient optimization. In this paper, we propose a novel colour image multilevel thresholding segmentation method based on the Trees Social Relationship Algorithm (TSR), a new metaheuristic algorithm inspired by the social and cooperative behaviour of trees in the forest. TSR mimics trees' hierarchical and collective life and uses four operators: growth, reproduction, competition, and death. We use TSR to optimize Kapur’s entropy as the objective function, which measures the information content of the segmented image. We compare the performance of our method with other established metaheuristic algorithms, including the Particle Swarm Optimization Algorithm (PSO), Artificial Bee Colony (ABC), Bat Optimization (BAT), Bacterial Foraging Algorithm (BFO), Backtracking Search Optimization Algorithm (BSA), Cuckoo Search (Cuckoo), Differential Evolution (DE), Electromagnetic Field Optimization (EFO), Firefly Algorithm (FA), and Wind Driven Optimization (WDO), on several benchmark colour images. We also use various evaluation metrics such as GCE, PRI, VOI, PSNR, FSIM, and SSIM to assess the quality of the segmentation results. The experimental results show that our method achieves better results than the other algorithms in accuracy, robustness, and convergence speed. Multilevel Thresholding Segmentation Image Segmentation Optimization Metaheuristic TSR Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4479475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310329199,"identity":"f7098379-542d-4ae6-9e5f-6dd8998d8a18","order_by":0,"name":"Soheil Fakheri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBAC9gY2NiAlAUYHHlRIQMUNcGvhOYCsJeEM8VoguhgS24hwGA/7sbQHH/dY2PNLNx88kDjPQt7gAPPDDwwF93Br4Uk7bjjjmUTizDnHEg4kbpMw3HCAzViCwaAYpxZ7hvQ2aZ4DEgkGN3IMQFoYNxxgMAP6JQG3LfzP26T/HJCwN7iR/+FA4hwJ+w0H2L/h1yKRdkya4QDQ8Bs5DAcSGyQSNxzgIWCLxLM0yZ4DQL/MSDM4kHBMInnmYZ5ioDvxOSzNTOLHgTp7fonkxx8+1NTZ9h1v3/jhwx/cWrAAZiAmScMoGAWjYBSMAgwAAIIiUsBJ5okRAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic Azad University","correspondingAuthor":true,"prefix":"","firstName":"Soheil","middleName":"","lastName":"Fakheri","suffix":""},{"id":310329200,"identity":"a6db6c64-9ea4-4df9-afe2-a4d2e2748022","order_by":1,"name":"Mahmoud Alimoradi","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Alimoradi","suffix":""},{"id":310329201,"identity":"405061cc-3d05-450a-a844-c9791e4b343a","order_by":2,"name":"Mohammad Reza Yamaghani","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Reza","lastName":"Yamaghani","suffix":""}],"badges":[],"createdAt":"2024-05-26 09:26:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4479475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4479475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58894533,"identity":"7be333e3-4c27-42b4-bcd5-dbe0ec201fcb","added_by":"auto","created_at":"2024-06-23 16:01:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5459710,"visible":true,"origin":"","legend":"","description":"","filename":"ColorImageMultilevelThresholdingSegmentationUsingTreesSocial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4479475/v1_covered_a1982b43-c651-48a7-9665-193ac9c7a37e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multilevel Thresholding Segmentation, Image Segmentation, Optimization, Metaheuristic, TSR","lastPublishedDoi":"10.21203/rs.3.rs-4479475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4479475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Colour image segmentation is an essential task in image processing and computer vision that aims to divide an image into meaningful and homogeneous regions. 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