Affiliations: [a] Department of Opto-Electronic Engineering, Changchun University of Science and Technology, Changchun, Jilin, China
| [b] Department of Equipment Engineering, Shenyang Ligong University, Shenyang, China
Correspondence:
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Corresponding author. Le Zhang, E-mail: [email protected].
Abstract: The purpose of image segmentation is to select the target region from the existing image, which is the core technology for image understanding, description and analysis. When faced with some complicated problems, the image segmentation effect of the traditional method is often unsatisfactory. As a branch of the swarm intelligence optimization algorithm, Particle Swarm Optimization (PSO) provides a new power and direction for the development of image segmentation. However, the algorithm has a large probability of loss of particle diversity in the late stage, which makes the algorithm converge prematurely. Therefore, the purpose of this paper is to improve the problem existing in the PSO algorithm and apply the improved algorithm in image segmentation. In this paper, the whole population of PSO algorithm is divided into multiple sub-populations and co-evolution. The mutation operation from the genetic algorithm is introduced at the same time. The worst sub-population is mutated according to the mutation probability. The larger inertia factor is selected to speed the particles. Update, and then carry out simulation experiments on some classical test functions. Finally, combined with the improved PSO algorithm and fuzzy C-means clustering algorithm (FCM), the fuzzy clustering validity index is introduced, and the blood cell image is segmented by the algorithm. The experimental results show that the algorithm can find a reasonable number of cluster center segmentation categories and efficiently perform adaptive segmentation of images.
