A Region Competitive Level Set method with Error Corrected Code Output Multiclass SVM and Binary Arithmetic Optimization for Stroke Lesion Segmentation

A Region Competitive Level Set method with Error Corrected Code Output Multiclass SVM and Binary Arithmetic Optimization for Stroke Lesion 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 Research Article A Region Competitive Level Set method with Error Corrected Code Output Multiclass SVM and Binary Arithmetic Optimization for Stroke Lesion Segmentation senthil kumar thiyagarajan, kalpana murugan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7789831/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 Stroke is one of the largest contributors to death and disability across the world. There is increasing demand for automation methods that detect and characterize different classes of stroke. Unsupervised region of interest extraction through clustering algorithms like K-means, Fuzzy C-means and their variants exhibits degradation when these algorithms go through image modalities with intensity inhomogeneity, noise and traps at local maximum. Active contour-based level set method is widely used in medical imaging as it accounts for fine-tuning the segmentation results from clustering approaches. These level set image segmentation methods fail in their evolution-convergence process when they are supplied with images having weak boundaries and a random initial contour over a fixed region of interest. These issues are addressed by our proposed Level set framework, which uses Error Correcting Code Output Multi Class Support Vector Machine for identifying stroke lesion group from the output of the FCM algorithm followed by region competition using binary arithmetic optimization-based pixel fitness detector for evolution and convergence of the level set. Encouraging Segmentation results are obtained in the proposed method with average values of 98.87% accuracy, 76.8% sensitivity, 83.1% Dice and 91.9% precision. Nuclear Medicine & Medical Imaging Artificial Intelligence and Machine Learning GM-Grey Matter WM-White Matter CSF-Cerebral Spinal Fluid ECCO-MC-SVM -Error Corrected Code Output Multi Class Support Vector Machines BAO-Binary Arithmetic Optimization MRI-Magnetic Resonance Image DWI-Diffusion Weighted Image ECCO-MC-SVM-BAO-RCLS - Error Corrected Code Output Multi Class Support Vector Machines and Binary Arithmetic Optimization based Region Competitive Level Set Ischemic Stroke Lesion Segmentation Full Text Additional Declarations The authors declare no competing interests. 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. 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