Abstract
This paper proposes an algorithm to measure the
width of retinal vessels in fundus photographs using graph-based
algorithm to segment both vessel edges simultaneously. First, the
simultaneous two-boundary segmentation problem is modeled as
a two-slice, three-dimension surface segmentation problem, which
is further converted into the problem of computing a minimum
closed set in a node-weighted graph. An initial segmentation is
generated from a vessel probability image. We use the REVIEW
database to evaluate diameter measurement performance. The
algorithm is robust and estimates the vessel width with subpixel
accuracy. The method is used to explore the relationship between
the average vessel width and the distance from the optic disc in
600 subjects.
Index Terms—retinal photography, graph-based segmentation,
vessel width measurement.
I. INTRODUCTION
A. Motivation
Complications of cardiovascular disease, such as stroke and
myocardial infarction, have high mortality and morbidity. The
retinal blood vessels are the vessels that are the easiest to
image non-invasively, and it has been shown that a decreased
ratio of arterial to venous retinal vessel width (the AV ratio)
forms an independent risk factor for stroke and myocardial
infarct, as well as for eye disease [1]–[3]. In retinal images,
the boundaries of the blood column form a reliable proxy for
vessel diameter. Automated determination of the AV-ratio is
therefore of high interest, but also complicated, because retinal
vessel width and the contrast to the background vary greatly
across retinal images. We have previously demonstrated a
fully automated algorithm for determination of the AV ratio
from fundus color photographs, by detecting the retinal
blood vessels, determining whether these are arteries or veins,
measuring their width, and determining the AV-ratio in an
automatically determined region of interest [4]. In a previous
study, we used a splat based technique to determine vessel
width [5]. However, graph-based approaches to determine the
location of the vessel boundary have the potential for greater
speed and accuracy, as they are known to be globally optimal
[6], [7]. In addition to AV-ratio analysis, automated determination
of vessel width measurement based on segmentation
of both vessel boundaries would also allow the geometry
of the retinal vessels to be quantified, as the geometry is
also affected by cardiovascular disease, diabetes, and retinal
disease. Finally, accurate determination of the vessel boundary
may allow local pathologic vascular changes such as tortuosity
and vessel beading to be measured accurately

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