Maximum entropy segmentation of improved particle swarm optimization

The most common method of image segmentation is the maximum entropy threshold segmentation, which is simple to implement and easy to segment. The standard maximum entropy segmentation has the problems of slow operation speed and low efficiency. Therefore, particle swarm optimization (PSO) is used to optimize the optimal threshold vector of the maximum entropy threshold segmentation. However, the traditional particle swarm optimization algorithm have some shortcomings, such as fall into dimensional disaster and premature convergence. Therefore, a maximum entropy segmentation algorithm based on improved particle swarm optimization is proposed. Improved particle swarm optimization uses multiple one-dimensional combinations to replace the original multi-dimensional group. Information exchange between these one-dimensional particle swarms produces the overall fitness value of the particle swarm, and then replace the worst particles with the best particles in each one-dimensional group, thereby eliminating premature convergence. Finally, the method is applied to liver image segmentation and compared with the standard particle swarm maximum entropy threshold segmentation. The results show that this improvement has better threshold and faster convergence.

I. Introduction

Image segmentation is one of the most useful techniques^[1]. Its purpose is to segment target area from background area, which provides an effective basis for subsequent recognition and analysis. This has been regarded as an important field of research by many domestic and foreign scholars^[2]. At present, The image segmentation method includes region-based threshold segmentation and edge detection-based segmentation^[3]. Among them, image threshold segmentation is widely used because of its simple and efficient performance and stable performance. The difficulty of threshold segmentation technology lies in the selection of threshold value, and the segmentation method based on maximum entropy is widely used in technology production because of its high practical value.