Paper Info
Title
Retinal Artery and Vein Classification for Automatic Vessel Caliber Grading.
Abstract
Automated retinal artery and vein identification is a necessity to measure their caliber automatically and to achieve high efficiency and repeatability for a large number of images. In this paper, a novel framework for retinal artery and vein classification is provided. The proposed method utilizes the vessel crossover and color intensity profile which are the most significant features for artery and vein classification. The method first extracts retinal vascular network and then identify individual blood vessels for further classification as artery or vein. We apply deep learning algorithm based segmentation method to extract the retinal vascular network. We then identify each blood vessels to measure caliber that will be used for computing the Central Retinal Artery Equivalent (CRAE) and Central Retinal Vein Equivalent (CRVE). We map the vessel network and use the individual vessel crossover information, vessel color and intensity profile to identify individual vessel segment as artery and vein. We compared automatically classified artery and vein results with a human grader which showed an accuracy of 95%. We compare our results of caliber grading against an established semi-automated caliber grading system and protocol which showed a very high correlation of 0.85 and 0.92, for CRAE and CRVE respectively.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512287
EMBC
Field
DocType
Volume
Biomedical engineering,Artery,Computer vision,Caliber,Retina,Computer science,Central retinal artery,Vein,Retinal Artery,Central retinal vein,Artificial intelligence,Retinal
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Alauddin Bhuiyan114716.34
Md. Akter Hussain282.88
Tien Yin Wong338938.10
Ronald Klein4204.78