Automatic multi-threshold image segmentation using metaheuristic algorithms

In this paper is presented an automatic segmentation approach for gray level images based on usage of metaheuristic swarming algorithms for multiple thresholds computing. The multi-threshold segmentation is an optimization problem while the thresholds must be determined and applied to the source image by minimizing an error measure. Because the number of possible solution may be very large in case of multiple thresholds, we used four metaheuristic swarming algorithms to obtain faster the optimal solution of the segmentation problem: Bacterial Foraging, Particle Swarming, Multi Swarm and Firefly optimization. As optimization criteria, root mean square error, peak signal-to-noise ratio and structural similarity index are used. Each optimization algorithm allows obtaining the optimal solution in a reasonable number of iterations and the obtained results were compared.