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Title: Segmentation of Retinal Blood Vessel Using Gabor Filter and Extreme Learning Machines
Authors: Aslan, Muhammet Fatih
Ceylan, Murat
Durdu, Akif
Keywords: Feature extraction
vessel segmentation
Extreme learning machine
Gabor filter
Top-Hat transform
Issue Date: 2018
Publisher: IEEE
Abstract: The 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.
Description: International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEY
ISBN: 978-1-5386-6878-8
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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