Abstract: Aiming at the problems that current image segmentation methods were difficult to achieve accurate and fast image segmentation, we proposed a fast image segmentation method for controlling the evolution of active contour. Firstly, the weighted sum of external energy and internal energy was used as the curve energy function, and the active contour wave model was established by using the external and internal energy
 of closed curve. Secondly, the curve energy was updated by moving the optimal path to obtain the required image segmentation target. Finally, the particle swarm optimization algorithm was introduced to obtain the optimal control points of all the initial contour points, and the goal of accurate segmentation of the target image was achieved by controlling the evolution of the active contour according to the optimal control points. The experimental results show that the image segmentation accuracy of the method is significantly higher than that of the current typical image segmentation method, which improves the anti noise performance and the speed of image segmentation.

Key words: particle swarm optimization algorithm, active contour wave model, image segmentation, energy function, fitness function