{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T02:03:22Z","timestamp":1776132202478,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T00:00:00Z","timestamp":1676332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye\u2019s retina and is a leading source of blindness. The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may be challenging if the lesion is small or if Optical Coherence Tomography (OCT) images are degraded by projection and motion. This paper aims to develop an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. OCT angiography is a non-invasive imaging tool that visualizes retinal and choroidal physiological and pathological vascularization. The presented system is based on new retinal layers in the OCT image-specific macular diseases feature extractor, including Multi-Size Kernels \u03becho-Weighted Median Patterns (MSK\u03beMP). Computer simulations show that the proposed method: (i) outperforms current state-of-the-art methods, including deep learning techniques; and (ii) achieves an overall accuracy of 99% using ten-fold cross-validation on the Duke University dataset and over 96% on the noisy Noor Eye Hospital dataset. In addition, MSK\u03beMP performs well in binary eye disease classifications and is more accurate than recent works in image texture descriptors.<\/jats:p>","DOI":"10.3390\/diagnostics13040729","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T03:37:24Z","timestamp":1676432244000},"page":"729","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Distinctions between Choroidal Neovascularization and Age Macular Degeneration in Ocular Disease Predictions via Multi-Size Kernels \u03becho-Weighted Median Patterns"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-2235","authenticated-orcid":false,"given":"Alex","family":"Liew","sequence":"first","affiliation":[{"name":"Department of Computer Science, Graduate Center of City University New York, 365 5th Ave., New York, NY 10016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Graduate Center of City University New York, 365 5th Ave., New York, NY 10016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samir","family":"Benbelkacem","sequence":"additional","affiliation":[{"name":"Robotics and Industrial Automation Division, Centre de D\u00e9veloppement des Technologies Avanc\u00e9es (CDTA), Algiers 16081, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.5626\/JCSE.2021.15.1.34","article-title":"Automated Detection of Age-Related Macular Degeneration from OCT Images Using Multipath CNN","volume":"15","author":"Thomas","year":"2021","journal-title":"J. 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