{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:07:53Z","timestamp":1769551673045,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T00:00:00Z","timestamp":1553126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Diabetic retinopathy (DR) is one of the most common causes of visual loss in developed countries. Computer-aided diagnosis systems aimed at detecting DR can reduce the workload of ophthalmologists in screening programs. Nevertheless, a large number of retinal images cannot be analyzed by physicians and automatic methods due to poor quality. Automatic retinal image quality assessment (RIQA) is needed before image analysis. The purpose of this study was to combine novel generic quality features to develop a RIQA method. Several features were calculated from retinal images to achieve this goal. Features derived from the spatial and spectral entropy-based quality (SSEQ) and the natural images quality evaluator (NIQE) methods were extracted. They were combined with novel sharpness and luminosity measures based on the continuous wavelet transform (CWT) and the hue saturation value (HSV) color model, respectively. A subset of non-redundant features was selected using the fast correlation-based filter (FCBF) method. Subsequently, a multilayer perceptron (MLP) neural network was used to obtain the quality of images from the selected features. Classification results achieved 91.46% accuracy, 92.04% sensitivity, and 87.92% specificity. Results suggest that the proposed RIQA method could be applied in a more general computer-aided diagnosis system aimed at detecting a variety of retinal pathologies such as DR and age-related macular degeneration.<\/jats:p>","DOI":"10.3390\/e21030311","type":"journal-article","created":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T12:28:01Z","timestamp":1553171281000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Combination of Global Features for the Automatic Quality Assessment of Retinal Images"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5540-6778","authenticated-orcid":false,"given":"Jorge","family":"Jim\u00e9nez-Garc\u00eda","sequence":"first","affiliation":[{"name":"Biomedical Engineering Group, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2996-4754","authenticated-orcid":false,"given":"Roberto","family":"Romero-Ora\u00e1","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4037-0351","authenticated-orcid":false,"given":"Mar\u00eda","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-287X","authenticated-orcid":false,"given":"Mar\u00eda I.","family":"L\u00f3pez-G\u00e1lvez","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"},{"name":"Department of Ophthalmology, Hospital Cl\u00ednico Universitario de Valladolid, Avenida Ram\u00f3n y Cajal 3, 47003 Valladolid, Spain"},{"name":"Instituto de Oftalmobiolog\u00eda Aplicada, University of Valladolid, Paseo de Bel\u00e9n 17, 47011 Valladolid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9915-2570","authenticated-orcid":false,"given":"Roberto","family":"Hornero","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Group, University of Valladolid, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"},{"name":"Instituto de Investigaci\u00f3n en Matem\u00e1ticas (IMUVA), University of Valladolid, 47011 Valladolid, Spain"},{"name":"Instituto de Neurociencias de Castilla y Le\u00f3n (INCYL), University of Salamanca, 37007 Salamanca, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal Imaging and Image Analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. 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