Particle Swarm Optimization Approach for the Segmentation of Retinal Vessels from Fundus Images

In this paper, we propose to use the Particle Swarm Optimization (PSO) algorithm to improve the Multi-Scale Line Detection (MSLD) method for the retinal blood vessel segmentation problem. The PSO algorithm is applied to find the best arrangement of scales in the basic line detector method. The segmentation performance was validated using a public high-resolution fundus images database containing healthy subjects. The optimized MSLD method demonstrates fast convergence to the optimal solution reducing the execution time by approximately 35%. For the same level of specificity, the proposed approach improves the sensitivity rate by 3.1% compared to the original MSLD method. The proposed method will allow to reduce the amount of missing vessels segments that might lead to false positives of red lesions detection in CAD systems used for diabetic retinopathy diagnosis.