The prevalence of diabetes is expected to increase dramatically in coming years; already today it accounts for
a major proportion of the health care budget in many countries. Diabetic Retinopathy (DR), a micro vascular
complication very often seen in diabetes patients, is the most common cause of visual loss in working age population
of developed countries today. Since the possibility of slowing or even stopping the progress of this disease
depends on the early detection of DR, an automatic analysis of fundus images would be of great help to the
ophthalmologist due to the small size of the symptoms and the large number of patients. An important symptom
for DR are abnormally wide veins leading to an unusually low ratio of the average diameter of arteries to veins
(AVR). There are also other diseases like high blood pressure or diseases of the pancreas with one symptom being
an abnormal AVR value. To determine it, a classification of vessels as arteries or veins is indispensable. As to our
knowledge despite the importance there have only been two approaches to vessel classification yet. Therefore we
propose an improved method. We compare two feature extraction methods and two classification methods based
on support vector machines and neural networks. Given a hand-segmentation of vessels our approach achieves
95.32% correctly classified vessel pixels. This value decreases by 10% on average, if the result of a segmentation
algorithm is used as basis for the classification.
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