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ABSTRACT
We focuses on the detection of retinal blood vessels which play a vital role in
reducing the proliferative diabetic retinopathy and for preventing the loss of
visual capability.
The proposed algorithm which takes advantage of the powerful pre-processing
techniques such as the contrast enhancement and thresholding offers an
automated segmentation procedure for retinal blood vessels. To evaluate the
performance of the new algorithm, experiments are conducted on 40 images
collected from DRIVE database.
The results show that the proposed algorithm performs better than the other
known algorithms in terms of accuracy. Furthermore, the proposed algorithm
being simple and easy to implement, is best suited for fast processing
applications.
Due to the rapid development in computing technology and techniques,
algorithms that support automated medical diagnosis have been gaining
importance. Retinal vasculature has received attention by specialists in
different pathologies, where the detection and analysis of retinal vasculature
may lead to early diagnosis and prevention of several diseases, such as
hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke.
One of the well-known and commonest diseases that need a computer-aided
medical diagnosis is diabetic retinopathy (DR), which leads in most cases to
partial or even complete loss of visual capability.
The accurate diagnosis of this disease depends upon some features which have
to be analyzed in order to quantify the severity level of the disease. Retinal
blood vessels are considered as one of the most important features for the
detection of DR. As diabetic retinopathy is a progressive disease, regular
screening of the human retina is essential for reducing the proliferative
diabetic retinopathy and for preventing the subsequent loss of visual
capability.
The screening should be done every 6 months, which includes obtaining and
analysing a sequence of fundus images and observing the early changes in
blood vessel patterns as well as the presence of micro aneurysms
