Retinal Vessel Segmentation Using Math-Inspired Metaheuristic Algorithms

Artificial intelligence-based biomedical image processing has become an important area of research in recent decades. In this context, one of the most important problems encountered is the close contrast values between the pixels to be segmented in the image and the remaining pixels. Among the crucial advantages provided by metaheuristic algorithms, they are generally able to provide better performances in the segmentation of biomedical images due to their randomized and gradient-free global search abilities. Math-inspired metaheuristic algorithms can be considered to be one of the most robust groups of algorithms, while also generally presenting non-complex structures. In this work, the recently proposed Circle Search Algorithm (CSA), Tangent Search Algorithm (TSA), Arithmetic Optimization Algorithm (AOA), Generalized Normal Distribution Optimization (GNDO), Global Optimization Method based on Clustering and Parabolic Approximation (GOBC-PA), and Sine Cosine Algorithm (SCA) were implemented for clustering and then applied to the retinal vessel segmentation task on retinal images from the DRIVE and STARE databases. Firstly, the segmentation results of each algorithm were obtained and compared with each other. Then, to compare the statistical performances of the algorithms, analyses were carried out in terms of sensitivity (Se), specificity (Sp), accuracy (Acc), standard deviation, and the Wilcoxon rank-sum test results. Finally, detailed convergence analyses were also carried out in terms of the convergence speed, mean squared error (MSE), CPU time, and number of function evaluations (NFEs) metrics.