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.

Biomedical image analysis based on metaheuristic algorithms is one of the most important research areas encountered in recent years. Due to the low contrast differences between the diseased areas and the image background in high-contrast biomedical images, effective methods are required to diagnose diseases with high accuracy. To overcome the difficulties encountered in this field, metaheuristic approaches may offer effective solutions due to their advantages such as the ability of converging to the global optimum, higher convergence rate, and having few control parameters. In this work, Jellyfish Search (JS), Marine Predators (MPA), Tunicate Swarm (TSA), Mayfly Optimization (MA), Chimp Optimization (ChOA), Slime Mould Optimization (SMA), Archimedes Optimization (AOA), and Equilibrium Optimizer (EO) algorithms, which are the most recently proposed metaheuristic algorithms in the literature, have been improved as clustering based in order to achieve vessel segmentation with high precision. Also, a detailed performance comparison of these algorithms has been realized for the rate of convergences, error values reached, CPU time, standard deviation, sensitivity, specificity, accuracy, F-score, and Wilcoxon rank sum-test. In order to present the compatibility of the results obtained with the literature, the performances of these novel algorithms have also been compared to that of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Differential Evolution (DE) algorithms. The simulation results represent that each algorithm produces similar convergence and error performance. Also, it can be emphasized from the statistical analyses that the stability and robustness of each metaheuristic approach are quite adequate in separating the vessel pixels and the background pixels of a retinal image. In general, this paper proves that although having fewer number of control parameters, the JS, MPA, TSA, MA, ChOA, SMA, AOA, and EO algorithms produce similar but a bit better results in terms of image segmentation when compared to PSO, GWO, and DE algorithms.

Structural changes in the retinal blood vessels provide important information about retinal diseases. Therefore, computer-aided segmentation of retinal blood vessels has become an active area of research in last decades. Due to the close contrast between the retinal blood vessels and the retinal background, robust methods should be developed to detect retinal blood vessels with high accuracy. In this work, artificial bee colony (ABC) algorithm which provides effective solutions to engineering problems has been applied to the retinal vessel segmentation. Clustering based ABC (basic ABC), quick-ABC (Q-ABC) and modified ABC (MR-ABC) algorithms have been analyzed for accurate segmentation of retinal blood vessels and their performances were compared. The simulations have been realized on the normal and abnormal retinal images taken from the DRIVE database. Simulation results and statistical analyses represent that ABC based approaches are stable and able to reach to optimal clustering performance with higher convergence rates. As a result it can be concluded that ABC based approaches can successfully be used for accurate segmentation of retinal blood vessels.