Journal article
Automated construction of arterial and venous trees in retinal images
Journal of Medical Imaging, Vol.2(4), pp.044001-044001
10/2015
DOI: 10.1117/1.JMI.2.4.044001
PMCID: PMC4652785
PMID: 26636114
Abstract
While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.
Details
- Title: Subtitle
- Automated construction of arterial and venous trees in retinal images
- Creators
- Qiao Hu - University of IowaMichael D Abràmoff - University of IowaMona K Garvin - Iowa City VA Health Care System
- Resource Type
- Journal article
- Publication Details
- Journal of Medical Imaging, Vol.2(4), pp.044001-044001
- Publisher
- Society of Photo-Optical Instrumentation Engineers
- DOI
- 10.1117/1.JMI.2.4.044001
- PMID
- 26636114
- PMCID
- PMC4652785
- ISSN
- 2329-4302
- eISSN
- 2329-4310
- Copyright
- © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
- Grant note
- I01 CX000119; IK2RX000728 / Department of Veterans Affairs Rehabilitation Research Development Division R01 EY018853; R01 EY023279 / NIH
- Language
- English
- Date published
- 10/2015
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
- Record Identifier
- 9983806245902771
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