To further improve the image segmentation accuracy,improving the traditional multi-threshold image segmentation method with large computation and slow segmentation,we proposed a multi-threshold image segmentation scheme.First,the initial solution is optimized by using the cubic chaotic mapping to improve the search efficiency.Then,scaling factors of the eagle perching optimizer (EPO) and crazy operators are introduced for perturbation and combined with position updates of the sparrow search algorithm (SSA),to improve the optimization accuracy,convergence rate and avoiding the local optimum.The improved seagull optimization algorithm (ISOA) is tested for performance using six benchmark functions.Finally,the ISOA is combined with threshold optimal selection for multi-threshold image segmentation based on Otsu and compared with existing segmentation algorithms.Simulation results show that the ISOA achieves the optimal value for 100% of the Otsu-based segmentation,and 80.9% outperforms the rest,optimizing both the segmentation accuracy and quality of the image.