为提升工程应用中图像分割的质量，在变异量子粒子群算法的基础上进行改进，并结合最大类间方差法提出了一种基于改进量子粒子群优化(QPSO)的多阈值图像分割算法． 该算法结合贝叶斯定理与粒子搜索过程中的历史信息构建了一个记忆向量，然后根据记忆向量对每个粒子的行为进行预测，并以此自动设置各粒子的变异概率，使算法在保持一定局部开发能力的同时提升全局搜索能力． 在 Berkeley 数据集上的仿真实验结果表明，与两种基于粒子群的图像分割算法相比，文中算法能获得更为稳定且清晰的图像分割结果．

In order to improve the quality of image segmentation in engineering applications，an improved quantum-behaved particle swarm optimization (QPSO) algorithm is proposed on the basis of mutated QPSO，which is then combined with the maximum between-cluster variance method to present a multi-threshold image segmentation algo-rithm． The algorithm is characterized by a memory vector constructed from memory information in the search proce-dure of particles using Bayesian theorem． The memory vector is used to predict the future behaviors of particles and to assign the mutation probability of each particle automatically． In this way，the global search ability is enhanced and the convergence ability is preserved for the algorithm． Experimental results on Berkeley datasets show that the proposed algorithm is superior to two existing PSO-based methods because it helps obtain more stable and clearer image segmentation results．