Image Processing

Automated construction of arterial and venous trees in retinal images

[+] Author Affiliations
Qiao Hu

University of Iowa, Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States

Michael D. Abràmoff

University of Iowa, Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States

University of Iowa, Department of Biomedical Engineering, 1402 Seamans Center, Iowa City, Iowa 52242, United States

University of Iowa, Department of Ophthalmology and Visual Sciences, 200 Hawkins Drive, Iowa City, Iowa 52242, United States

University of Iowa, Stephen A. Wynn Institute for Vision Research, 200 Hawkins Drive, Iowa City, Iowa 52242, United States

Mona K. Garvin

University of Iowa, Department of Electrical and Computer Engineering, 4016 Seamans Center, Iowa City, Iowa 52242, United States

Iowa City VA Health Care System, 601 Highway 6 West, Iowa City, Iowa 52246, United States

J. Med. Imag. 2(4), 044001 (Nov 19, 2015). doi:10.1117/1.JMI.2.4.044001
History: Received March 5, 2015; Accepted September 28, 2015
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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.

© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Qiao Hu ; Michael D. Abràmoff and Mona K. Garvin
"Automated construction of arterial and venous trees in retinal images", J. Med. Imag. 2(4), 044001 (Nov 19, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.044001


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