NDLI: Automatic Identification of Retinal Arteries and Veins From Dual-Wavelength Images Using Structural and Functional Features

Content Provider	IEEE Xplore Digital Library
Author	Narasimha-Iyer, H. Beach, J.M. Khoobehi, B. Roysam, B.
Copyright Year	1964
Abstract	This paper presents an automated method to identify arteries and veins in dual-wavelength retinal fundus images recorded at 570 and 600 nm. Dual-wavelength imaging provides both structural and functional features that can be exploited for identification. The processing begins with automated tracing of the vessels from the 570-nm image. The 600-nm image is registered to this image, and structural and functional features are computed for each vessel segment. We use the relative strength of the vessel central reflex as the structural feature. The central reflex phenomenon, caused by light reflection from vessel surfaces that are parallel to the incident light, is especially pronounced at longer wavelengths for arteries compared to veins. We use a dual-Gaussian to model the cross-sectional intensity profile of vessels. The model parameters are estimated using a robust -estimator, and the relative strength of the central reflex is computed from these parameters. The functional feature exploits the fact that arterial blood is more oxygenated relative to that in veins. This motivates use of the ratio of the vessel optical densities (ODs) from images at oxygen-sensitive and oxygen-insensitive wavelengths () as a functional indicator. Finally, the structural and functional features are combined in a classifier to identify the type of the vessel. We experimented with four different classifiers and the best result was given by a support vector machine (SVM) classifier. With the SVM classifier, the proposed algorithm achieved true positive rates of 97% for the arteries and 90% for the veins, when applied to a set of 251 vessel segments obtained from 25 dual wavelength images. The ability to identify the vessel type is useful in applications such as automated retinal vessel oximetry and automated analysis of vascular changes without manual intervention.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	9
File Size	1537541
Starting Page	1427
Ending Page	1435
File Format	PDF
ISSN	00189294
Volume Number	54
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Retina Arteries Veins Support vector machines Support vector machine classification Image segmentation Optical imaging Optical reflection Surface waves Optical surface waves vessel type Automated image analysis automatic identification retinal oximetry structural and functional features vessel profile modeling
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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