A 3D Spatial Color Space Transformation of Automated Glaucoma Detection

A 3D Spatial Color Space Transformation of Automated Glaucoma Detection | 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 3D Spatial Color Space Transformation of Automated Glaucoma Detection Sofia Sa'idah, Achmad Rizal, Inung Wijayanto, R Maula Rifada This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8374570/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 Glaucoma is a leading cause of irreversible blindness worldwide, and early detection is crucial for preventing progressive vision loss. Addressing this problem requires feature representations that can capture both the chromatic and structural alterations in the retina associated with glaucomatous damage. This study presents an automated glaucoma detection framework based on three-dimensional (3D) spatial color space transformation using the FIVES public dataset. The proposed system integrates image preprocessing, 3D reconstruction, and statistical analysis of multiple color spaces, including red, green, blue (RGB), hue, saturation, value (HSV), luma, blue chrominance, red chrominance (YCbCr), and luminance, a*, b* (Lab), to enhance the discriminative representation of the retinal structures. The intensity, texture and morphology-based features were computed and combined to capture both spatial and chromatic variations relevant to glaucomatous changes. Analysis of Variance (ANOVA) was applied prior to classification to reduce feature redundancy. Several machine-learning classifiers were evaluated to determine the most effective diagnostic performance. Experiments show that the combination of Lab and YCbCr with a nonlinear classifier provides the best performance, achieving an average accuracy of 0.87, sensitivity of 0.98, and specificity 0.92. Random Forest and k-nearest neighbor (k = 5) models consistently outperformed the parametric linear model across all four tested color spaces. These findings indicate that separating luminance and chrominance components and modeling nonlinear relationships between color and morphological features are crucial for improving the reliability of glaucoma detection based on fundus images, making the combination of RF and kNN a promising baseline configuration for automated glaucoma screening systems. Analysis of Variance Color spaces Fundus images Glaucoma 3D fundus images 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-8374570","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576312135,"identity":"c65a3ccf-b151-4292-886b-2db1e70d88d6","order_by":0,"name":"Sofia Sa'idah","email":"","orcid":"","institution":"Telkom University","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Sa'idah","suffix":""},{"id":576312137,"identity":"d1d05e0c-d0cc-492a-a8ef-916e43cd1eac","order_by":1,"name":"Achmad Rizal","email":"","orcid":"","institution":"Telkom 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Addressing this problem requires feature representations that can capture both the chromatic and structural alterations in the retina associated with glaucomatous damage. This study presents an automated glaucoma detection framework based on three-dimensional (3D) spatial color space transformation using the FIVES public dataset. The proposed system integrates image preprocessing, 3D reconstruction, and statistical analysis of multiple color spaces, including red, green, blue (RGB), hue, saturation, value (HSV), luma, blue chrominance, red chrominance (YCbCr), and luminance, a*, b* (Lab), to enhance the discriminative representation of the retinal structures. The intensity, texture and morphology-based features were computed and combined to capture both spatial and chromatic variations relevant to glaucomatous changes. Analysis of Variance (ANOVA) was applied prior to classification to reduce feature redundancy. Several machine-learning classifiers were evaluated to determine the most effective diagnostic performance. Experiments show that the combination of Lab and YCbCr with a nonlinear classifier provides the best performance, achieving an average accuracy of 0.87, sensitivity of 0.98, and specificity 0.92. Random Forest and k-nearest neighbor (k\u0026thinsp;=\u0026thinsp;5) models consistently outperformed the parametric linear model across all four tested color spaces. These findings indicate that separating luminance and chrominance components and modeling nonlinear relationships between color and morphological features are crucial for improving the reliability of glaucoma detection based on fundus images, making the combination of RF and kNN a promising baseline configuration for automated glaucoma screening systems.\u003c/p\u003e","manuscriptTitle":"A 3D Spatial Color Space Transformation of Automated Glaucoma Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:27:32","doi":"10.21203/rs.3.rs-8374570/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"abae6b21-990a-483a-84be-21d5950e0557","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T11:42:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:27:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8374570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8374570","identity":"rs-8374570","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}