A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network

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dc.contributor.authorCeylan, Murat
dc.contributor.authorYasar, Huseyin
dc.date.accessioned2020-03-26T19:22:56Z
dc.date.available2020-03-26T19:22:56Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study determined the features of line, curve, and ridge structures in images using complex ripplet-I and enabled extraction of blood vessel networks from retinal images through a complex valued artificial neural network using those features. Forty color fundus images in the DRIVE database and 20 color fundus images in the STARE database were used to test the success of the proposed system. In this study, a complex version of ripplet-I transform was used for the first time. By presenting the directed image for the determination of the unique geometrical properties of the vessel regions, complex ripplet-I transforms showing better performance than other types of multiresolution analysis were combined with a complex valued ANN. The results in the study were reobtained using leave -one -out cross validation method with bagging technique in order to ensure the stability and correctness of the performance. In the DRIVE database, the highest average accuracy of the system was found to be 98.44% for complex ripplet-I transform and complex valued ANN. For the STARE database (labeled by Adam Hoover), highest average accuracy rates were obtained as 99.25% for complex ripplet-I transforms and complex valued ANN. Similarly, for the other labeled data (by Valentina Kouznetsova), highest average accuracy rates were obtained as 98.03% for complex ripplet-I transforms and complex valued ANN.en_US
dc.identifier.doi10.3906/elk-1408-157en_US
dc.identifier.endpage3227en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issn1303-6203en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage3212en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1408-157
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33190
dc.identifier.volume24en_US
dc.identifier.wosWOS:000374325800081en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectComplex ripplet-I transformen_US
dc.subjectcomplex valued artificial neural networken_US
dc.subjectblood vessel extractionen_US
dc.subjectDRIVE databaseen_US
dc.subjectSTARE databaseen_US
dc.titleA novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural networken_US
dc.typeArticleen_US

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