Retinal vascular network topology reconstruction and artery/vein classification via dominant set clustering

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dc.contributor.authorZhao, Yitian
dc.contributor.authorXie, Jianyang
dc.contributor.authorZhang, Huaizhong
dc.contributor.authorZheng, Yalin
dc.contributor.authorZhao, Yifan
dc.contributor.authorQi, Hong
dc.contributor.authorZhao, Yangchun
dc.contributor.authorSu, Pan
dc.contributor.authorLiu, Jiang
dc.contributor.authorLiu, Yonghuai
dc.date.accessioned2019-08-13T14:36:43Z
dc.date.available2019-08-13T14:36:43Z
dc.date.issued2019-07-03
dc.description.abstractThe estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, INSPIRE, IOSTAR, VICAVR, DRIVE and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community.en_UK
dc.identifier.citationZhao Y, Xie J, Zhang H, et al., (2020) Retinal vascular network topology reconstruction and artery/vein classification via dominant set clustering. IEEE Transactions on Medical Imaging, Volume 39, Issue 2, February 2020, pp. 341-356en_UK
dc.identifier.issn0278-0062
dc.identifier.urihttps://doi.org/10.1109/TMI.2019.2926492
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14428
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectRetinal imagesen_UK
dc.subjectdominant set clusteringen_UK
dc.subjectblood vesselen_UK
dc.subjectvascular topologyen_UK
dc.subjectArtery/vein classificationen_UK
dc.titleRetinal vascular network topology reconstruction and artery/vein classification via dominant set clusteringen_UK
dc.typeArticleen_UK

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