Retinal blood vessel segmentation using pixel-based feature vector

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridTOPTAŞ, Buket/0000-0003-2556-8199
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidTOPTAŞ, Buket/HTL-3938-2023
dc.contributor.authorToptas, Buket
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:50:33Z
dc.date.available2024-08-04T20:50:33Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractA lot of important disease information can be accessed by performing retinal blood vessel analysis on fundus images. Diabetic retinopathy is one of the diseases understood by retinal blood vessel analysis. If this disease is detected at an early stage, vision loss can be prevented. In this paper, a method that performs retinal blood vessel analysis with classical methods is proposed. In this proposed system, pixel-based feature extraction is performed. Five different feature groups are used for feature extraction. These feature groups are edge detection, morphological, statistical, gradient, and Hessian matrix. An 18-D feature vector is created for each pixel. This feature vector is given to the artificial neural network for training. Using test images, the system is tested on two publicly available datasets. Sensitivity, Specificity, and Accuracy performance measures were used as success measures. The similarity index between the segmented image and the ground truth is measure using Dice and Jaccard. The accuracy of the system was measured as 96.18% for DRIVE and 94.56% for STARE, respectively. Experimental results show that the proposed algorithm achieves satisfactory results. This method can be used as an automated retinal blood vessel segmenting system.en_US
dc.description.sponsorshipInonu university scientific research and coordination unit [FDK-2020-2109]en_US
dc.description.sponsorshipThis work was supported by the Inonu university scientific research and coordination unit [FDK-2020-2109]en_US
dc.identifier.doi10.1016/j.bspc.2021.103053
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85112521735en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.103053
dc.identifier.urihttps://hdl.handle.net/11616/100129
dc.identifier.volume70en_US
dc.identifier.wosWOS:000696962600009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical imagingen_US
dc.subjectRetinal blood vessel segmentationen_US
dc.subjectImage segmentationen_US
dc.subjectFeature extractionen_US
dc.titleRetinal blood vessel segmentation using pixel-based feature vectoren_US
dc.typeArticleen_US

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