Abstract
In the area of image processing, segmentation of an image into multiple regions is very important for classification and recognition steps. It has been widely used in many application fields such as medical image analysis to characterize and detect anatomical structures, robotics features extraction for mobile robot localization and detection and map procession for lines and legends finding. Many techniques have been developed in the field of image segmentation. Methods based on intelligent techniques are the most used such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) called metaheuristics algorithms. In this paper, we describe a novel method for segmentation of images based on one of the most popular and efficient metaheuristic algorithm called Particle Swarm optimization (PSO) for determining multilevel threshold for a given image. The proposed method takes advantage of the characteristics of the particle swarm optimization and improves the objective function value to updating the velocity and the position of particles. This method is compared to the basic PSO method, also, it is compared with other known multilevel segmentation methods to demonstrate its efficiency. Experimental results show that this method can reliably segment and give threshold values than other methods considering different measures.