{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:32:57Z","timestamp":1776277977932,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2009,3,24]],"date-time":"2009-03-24T00:00:00Z","timestamp":1237852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists\u2019 hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.<\/jats:p>","DOI":"10.3390\/s90302148","type":"journal-article","created":{"date-parts":[[2009,3,26]],"date-time":"2009-03-26T13:02:18Z","timestamp":1238072538000},"page":"2148-2161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":158,"title":["Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering"],"prefix":"10.3390","volume":"9","author":[{"given":"Akara","family":"Sopharak","sequence":"first","affiliation":[{"name":"Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand"}]},{"given":"Bunyarit","family":"Uyyanonvara","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University 131 Moo 5, Tiwanont Road, Bangkadi, Muang, Pathumthani 12000, Thailand"}]},{"given":"Sarah","family":"Barman","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Information Systems and Mathematics, Kingston University Penrhyn Road, Kingston upon Thames, Surrey, KT1 2EE, UK"}]}],"member":"1968","published-online":{"date-parts":[[2009,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1046\/j.1464-5491.2003.00969.x","article-title":"A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy","volume":"20","author":"Olson","year":"2003","journal-title":"Diabet. 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