Scopus:
Blood vessel segmentation and extraction using H-minima method based on image processing techniques

dc.contributor.authorBoubakar Khalifa Albargathe S.M.
dc.contributor.authorKamberli E.
dc.contributor.authorKandemirli F.
dc.contributor.authorRahebi J.
dc.date.accessioned2023-04-12T01:01:12Z
dc.date.available2023-04-12T01:01:12Z
dc.date.issued2021-01-01
dc.description.abstractIn this paper, the H-minima transform is used for blood vessel segmentation. The aim of this study is to get the high accuracy of blood vessel segmentation in retinal images. In this study the good result and good performance were got. We compared our result with other methods. Also for simulation result we implemented on DRIVE and STARE database. The proposed method shows very remarkable performance on pathological retinal images. For the implementing of the proposed method MATLAB 2019a software is used. The running time of this method was 1 s for each image and the average accuracy for STARE dataset and DRIVE dataset achieved to 0.9591 and 0.9672 respectively.
dc.identifier.doi10.1007/s11042-020-09646-3
dc.identifier.issn13807501
dc.identifier.scopus2-s2.0-85091018185
dc.identifier.urihttps://hdl.handle.net/20.500.12597/4621
dc.relation.ispartofMultimedia Tools and Applications
dc.rightsfalse
dc.subjectFundus camera | Image processing | Retina image
dc.titleBlood vessel segmentation and extraction using H-minima method based on image processing techniques
dc.typeArticle
dspace.entity.typeScopus
oaire.citation.issue2
oaire.citation.volume80
person.affiliation.nameKastamonu University
person.affiliation.nameKastamonu University
person.affiliation.nameKastamonu University
person.affiliation.nameAltinbas Universitesi
person.identifier.scopus-author-id57219005352
person.identifier.scopus-author-id57224339257
person.identifier.scopus-author-id6602393314
person.identifier.scopus-author-id36451137000
relation.isPublicationOfScopus59a92e7a-6a3d-44b7-989c-23235e3830ed
relation.isPublicationOfScopus.latestForDiscovery59a92e7a-6a3d-44b7-989c-23235e3830ed

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