Multilevel Threshold Image Segmentation of Brain Tumors Using Zebra Optimization Algorithm

Multilevel Threshold Image Segmentation of Brain Tumors Using Zebra Optimization 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 Article Multilevel Threshold Image Segmentation of Brain Tumors Using Zebra Optimization Algorithm Sarah Alhammad, Doaa Khafaga, Doaa Elshoura, Khalid M. Hosny This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3941267/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 A Brain Tumor (BT), further known as an intracranial tumor, is a mass of abnormal tissue whose cells multiply and procreate uncontrolled and appear unaffected by those mechanisms that control normal cells, and it causes many people's deaths each year. BT is frequently detected using Magnetic Resonance Imaging (MRI) procedures. One of the greatest common techniques for segmenting medical images is Multilevel Thresholding (MT). MT received the researchers ' attention because of its simplicity, ease of use, and accuracy. Consequently, this paper uses the most recent Zebra Optimization Algorithm (ZOA) to deal with the MT problems of MRI images. The ZOA's performance has been evaluated on 10 MRI images with threshold levels up to 10 and evaluated against five different algorithms: Sine Cosine Algorithm (SCA), Arithmetic Optimization Algorithm (AOA), Flower Pollination Algorithm (FPA), Reptile Search Algorithm (RSA), and Marine Predators Algorithm (MPA). The experimental results, which included numerous performance metrics such as Mean Square Error (MSE), Peak Signal-To-Noise Ratio (PSNR), Feature Similarity Index Metric (FSIM), Normalized Correlation Coefficient (NCC), and fitness values, totally show that the ZOA outperforms all other algorithms based on Kapur's entropy for all the applied measures. Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Image segmentation Brain tumor Optimization Kapur’s entropy 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. 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