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Effective timing and treatment are critical to saving the sight of patients with diabetes. Lack of screening, as well as a
shortage of ophthalmologists, help contribute to approximately 8,000 cases per year of people who lose their sight to
diabetic retinopathy, the leading cause of new cases of blindness [1] [2]. Timely treatment for diabetic retinopathy
prevents severe vision loss in over 50% of eyes tested [1]. Fundus images can provide information for detecting and
monitoring eye-related diseases, like diabetic retinopathy, which if detected early, may help prevent vision loss.
Damaged blood vessels can indicate the presence of diabetic retinopathy [9]. So, early detection of damaged vessels in
retinal images can provide valuable information about the presence of disease, thereby helping to prevent vision loss.
Purpose: The purpose of this study was to compare the effectiveness of two blood vessel segmentation algorithms.
Methods: Fifteen fundus images from the STARE database were used to develop two algorithms using the CVIPtools
software environment. Another set of fifteen images were derived from the first fifteen and contained
ophthalmologists' hand-drawn tracings over the retinal vessels. The ophthalmologists' tracings were used as the "gold
standard" for perfect segmentation and compared with the segmented images that were output by the two algorithms.
Comparisons between the segmented and the hand-drawn images were made using Pratt's Figure of Merit (FOM),
Signal-to-Noise Ratio (SNR) and Root Mean Square (RMS) Error. Results: Algorithm 2 has an FOM that is 10%
higher than Algorithm 1. Algorithm 2 has a 6%-higher SNR than Algorithm 1. Algorithm 2 has only 1.3% more RMS
error than Algorithm 1. Conclusions: Algorithm 1 extracted most of the blood vessels with some missing intersections
and bifurcations. Algorithm 2 extracted all the major blood vessels, but eradicated some vessels as well. Algorithm 2
outperformed Algorithm 1 in terms of visual clarity, FOM and SNR. The performances of these algorithms show that
they have an appreciable amount of potential in helping ophthalmologists detect the severity of eye-related diseases
and prevent vision loss.
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