{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-recursive-bayesian-approach-to-describe","title":"A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry","arxiv_id":"1711.10521","date":"2017-11-28","proceeding":null,"authors":["Fatmatulzehra Uslu","Anil Anthony Bharath"],"abstract":"Demographic studies suggest that changes in the retinal vasculature geometry,\nespecially in vessel width, are associated with the incidence or progression of\neye-related or systemic diseases. To date, the main information source for\nwidth estimation from fundus images has been the intensity profile between\nvessel edges. However, there are many factors affecting the intensity profile:\npathologies, the central light reflex and local illumination levels, to name a\nfew. In this study, we introduce three information sources for width\nestimation. These are the probability profiles of vessel interior, centreline\nand edge locations generated by a deep network. The probability profiles\nprovide direct access to vessel geometry and are used in the likelihood\ncalculation for a Bayesian method, particle filtering. We also introduce a\ngeometric model which can handle non-ideal conditions of the probability\nprofiles. Our experiments conducted on the REVIEW dataset yielded consistent\nestimates of vessel width, even in cases when one of the vessel edges is\ndifficult to identify. Moreover, our results suggest that the method is better\nthan human observers at locating edges of low contrast vessels.","url_abs":"http://arxiv.org/abs/1711.10521v1","url_pdf":"http://arxiv.org/pdf/1711.10521v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-recursive-bayesian-approach-to-describe","repo_url":"https://bitbucket.org/fzehra/a-recursive-bayesian-approach-to-describe-retinal-vasculature","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}