A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches

• Published in: Biomedizinische Technik Vol. 66; no. 2; pp. 181 - 200
• Main Authors: Çetinkaya, Mehmet Bahadır, Duran, Hakan
• Format: Journal Article
• Language: English
• Published: Germany De Gruyter 27.04.2021; Walter de Gruyter GmbH
• Subjects: Algorithms; artificial bee colony algorithm; Blood vessels; Clustering; Convergence; differential evolution algorithm; Evolutionary computation; firefly algorithm; grey wolf optimization algorithm; harmony search algorithm; Heuristic; Image contrast; Image resolution; Image segmentation; Machine learning; Mean; Medical imaging; Medical treatment; Optimization; Particle swarm optimization; particle swarm optimization algorithm; Retina; Retinal images; retinal vessel segmentation; Search algorithms; Statistical analysis; teaching learning based optimization algorithm; artificial bee colony algorithm; grey wolf optimization algorithm; harmony search algorithm; differential evolution algorithm; teaching learning based optimization algorithm; firefly algorithm; retinal vessel segmentation; particle swarm optimization algorithm
• ISSN: 0013-5585; 1862-278X; 1862-278X
• DOI: 10.1515/bmt-2020-0089

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.