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There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.<\/jats:p>","DOI":"10.1371\/journal.pone.0288792","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T13:56:19Z","timestamp":1689774979000},"page":"e0288792","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":25,"title":["Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy"],"prefix":"10.1371","volume":"18","author":[{"given":"Hesham","family":"Abdushkour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8560-0026","authenticated-orcid":true,"given":"Toufique A.","family":"Soomro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2645-7258","authenticated-orcid":true,"given":"Ahmed","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayyaz","family":"Ali Jandan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herbert","family":"Jelinek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farida","family":"Memon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Althobiani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleh","family":"Mohammed Ghonaim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"pone.0288792.ref001","doi-asserted-by":"crossref","first-page":"101905","DOI":"10.1016\/j.media.2020.101905","article-title":"A review of machine learning methods for retinal blood vessel segmentation and artery\/vein classifcation","volume":"68","author":"MRK Mookiah","year":"2021","journal-title":"Med Image Anal"},{"issue":"1","key":"pone.0288792.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13721-021-00294-7","article-title":"Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images","volume":"10","author":"AA Abdulsahib","year":"2021","journal-title":"Netw. 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