{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:51:30Z","timestamp":1778320290826,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,19]],"date-time":"2017-10-19T00:00:00Z","timestamp":1508371200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image\u2019s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.<\/jats:p>","DOI":"10.3390\/sym9100235","type":"journal-article","created":{"date-parts":[[2017,10,19]],"date-time":"2017-10-19T11:07:29Z","timestamp":1508411249000},"page":"235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-9682","authenticated-orcid":false,"given":"Yanhui","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Illinois at Springfield, Springfield, IL 62703, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4082-383X","authenticated-orcid":false,"given":"\u00dcmit","family":"Budak","sequence":"additional","affiliation":[{"name":"Engineering Faculty, Department of Electrical-Electronics Engineering, Bitlis Eren University, 13000 Bitlis, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1614-2639","authenticated-orcid":false,"given":"Abdulkadir","family":"\u015eeng\u00fcr","sequence":"additional","affiliation":[{"name":"Technology Faculty, Department of Electrical and Electronics Engineering, Firat University, 23119 Elazig, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-5926","authenticated-orcid":false,"given":"Florentin","family":"Smarandache","sequence":"additional","affiliation":[{"name":"Mathematics & Science Department, University of New Mexico, 705 Gurley Ave., Gallup, NM 87301, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,19]]},"reference":[{"key":"ref_1","unstructured":"Kanski, J.J. 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