Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/10505
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSrinidhi, C.L.-
dc.contributor.authorAparna, P.-
dc.contributor.authorRajan, J.-
dc.date.accessioned2020-03-31T08:19:21Z-
dc.date.available2020-03-31T08:19:21Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Image Processing, 2019, Vol.28, 6, pp.2705-2718en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/10505-
dc.description.abstractSeparation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8%, and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR, and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming the state-of-The-Art methods for A/V separation. 1992-2012 IEEE.en_US
dc.titleAutomated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approachen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

Files in This Item:
File Description SizeFormat 
12.Automated Method.pdf5.34 MBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.