A Bayesian framework for the local configuration of retinal junctions

Qureshi, Touseef Ahmad, Hunter, Andrew and Al-Diri, Bashir (2014) A Bayesian framework for the local configuration of retinal junctions. In: IEEE CVPR, 23-28 June 2014, Ohio, USA.

Full content URL: https://doi.org/10.1109/CVPR.2014.397

A Bayesian framework for the local configuration of retinal junctions
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Retinal images contain forests of mutually intersecting and overlapping venous and arterial vascular trees. The geometry of these trees shows adaptation to vascular diseases including diabetes, stroke and hypertension. Segmentation of the retinal vascular network is complicated by inconsistent vessel contrast, fuzzy edges, variable image quality, media opacities, complex intersections and overlaps. This paper presents a Bayesian approach to resolving the con- figuration of vascular junctions to correctly construct the vascular trees. A probabilistic model of vascular joints (terminals, bridges and bifurcations) and their configuration in junctions is built, and Maximum A Posteriori (MAP) estimation used to select most likely configurations. The model is built using a reference set of 3010 joints extracted from the DRIVE public domain vascular segmentation dataset, and evaluated on 3435 joints from the DRIVE test set, demonstrating an accuracy of 95.2%.

Additional Information:Open Access version of paper supplied by Computer Vision Foundation
Keywords:Retinal vessels configuration, vessels connectivity, junction resolution, vessels trees reconstruction
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G320 Probability
Divisions:College of Science > School of Computer Science
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ID Code:19685
Deposited On:27 Nov 2015 10:13

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