Abstract
Computer based imaging and analysis techniques are frequently used for the diagnosis and treatment of retinal diseases. Although retinal images are of high resolution, the contrast of the retinal blood vessels is usually very close to the background of the retinal image. The detection of the retinal blood vessels with low contrast or with contrast close to the background of the retinal image is too difficult. Therefore, improving algorithms which can successfully distinguish retinal blood vessels from the retinal image has become an important area of research. In this work, clustering based heuristic artificial bee colony, particle swarm optimization, differential evolution, teaching learning based optimization, grey wolf optimization, firefly and harmony search algorithms were applied for accurate segmentation of retinal vessels and their performances were compared in terms of convergence speed, mean squared error, standard deviation, sensitivity, specificity. accuracy and precision. From the simulation results it is seen that the performance of the algorithms in terms of convergence speed and mean squared error is close to each other. It is observed from the statistical analyses that the algorithms show stable behavior and also the vessel and the background pixels of the retinal image can successfully be clustered by the heuristic algorithms.
Highlights
Retinal vessel segmentation is one of the most important areas of retinal image analysis because some attributes of retinal vessels are usually important symptoms of diseases [1]
Novel approaches based on artificial bee colony (ABC), particle swarm optimization (PSO), differential evolution (DE), teaching learning based optimization (TLBO), grey wolf optimization (GWO), FA and harmony search (HS) algorithms for clustering based retinal vessel segmentation is described in the fundus fluorescein angiography retinal images
It is seen from the simulation results that each algorithm converge to the global solutions at similar cycles and the final Mean-Squared Error (MSE) error values reached by the algorithms are very close to each other
Summary
Retinal vessel segmentation is one of the most important areas of retinal image analysis because some attributes of retinal vessels are usually important symptoms of diseases [1]. Automated segmentation of retinal vessels is accepted as the first step of developments in the area of computer-aided diagnosis systems for ophthalmic disorders [2], The abnormalities caused by diseases of obesity [3], hypertension [4], glaucoma [5] and diabetic retinopathy [6,7,8] are able to display with higher accuracy by means of the automated segmentation of retinal vessels. It plays an important role in the areas such as evaluation of retinopathy of prematurity [9], vessel diameter measurement [10]. The normal and abnormal retinal images taken from DRIVE and STARE databases are given in Figures 1 and 2, respectively
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