A New Image Segmentation Method Based on Particle Swarm Optimization

In this paper, a new segmentation method for images based on particle swarm optimization (PSO) is proposed. The new method is produced through combining PSO algorithm with one of region-based image segmentation methods, which is named Seeded Region Growing (SRG).The algorithm of SRG method performs a segmentation of an image with respect to a set of points known as seeds. Two problems are related with SRG method, the first one is the choice of the similarity criteria of pixels in regions and the second problem is how to select the seeds. In the proposed method, PSO algorithm tries to solve the two problems of SRG method. The similarity criteria that will be solved is the best similarity difference between the pixel intensity and the region mean value. The proposed algorithm randomly initialise each particle in the swarm to contain K seed points (each seed point contains its location and similarity difference value) and then SRG algorithm is applied to each particle. PSO technique is then applied to refine the locations and similarity difference values of the K seed points. Finally, region merging is applied to remove small regions from the segmented image.