Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/155
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dc.contributor.authorAslan, Muhammet Fatih-
dc.contributor.authorCeylan, Murat-
dc.contributor.authorDurdu, Akif-
dc.date.accessioned2021-12-13T10:19:52Z-
dc.date.available2021-12-13T10:19:52Z-
dc.date.issued2018-
dc.identifier.isbn978-1-5386-6878-8-
dc.identifier.urihttps://doi.org/10.1109/IDAP.2018.8620890-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/155-
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractThe process of obtaining blood vessels from the retinal fundus images plays an important role in the detection of disease in the eye. Analysis of blood vessels provides preliminary information on the presence and treatment of glaucoma, retinopathy, etc. This is why such practices are important. In this study, firstly, features were extracted from color retinal images. Adaptive threshold, Gabor filter and Top-Hat transform were used to make the blood vessel more visible during the feature extraction phase. Subsequently, the acquired features were given as input to the extreme learning machine, and as a result, retinal blood vessel was obtained. At this stage, DRIVE database was used. Twenty colored retinal fundus images were used in the train phase. Thanks to the extreme learning machine, the training process has been carried out quickly (0.42 sec). A high accuracy rate is obtained as %94.59.en_US
dc.description.sponsorshipInonu Univ, Comp Sci Dept, IEEE Turkey Sect, Anatolian Scien_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature extractionen_US
dc.subjectvessel segmentationen_US
dc.subjectExtreme learning machineen_US
dc.subjectGabor filteren_US
dc.subjectTop-Hat transformen_US
dc.subjectIMAGESen_US
dc.titleSegmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machinesen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1109/IDAP.2018.8620890-
dc.identifier.scopus2-s2.0-85062526933en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authoridDurdu, Akif/0000-0002-5611-2322-
dc.authorwosidDurdu, Akif/AAQ-4344-2020-
dc.authorwosidAslan, Muhammet Fatih/V-8019-2017-
dc.identifier.wosWOS:000458717400167en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.authorscopusid57205362915-
dc.authorscopusid56276648900-
dc.authorscopusid55364612200-
item.openairetypeConference Object-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextembargo_20300101-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
crisitem.author.dept02.04. Department of Electrical and Electronics Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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