A medical image segmentation method based on quantum-behaved particle swarm cooperative optimization mainly solves the problem that increasing of categories in the prior art of image segmentation results in overlong segmentation time, local optimum of segmentation results and low segmentation precision within bearable time. The technical scheme includes step one, reading in medical images to obtain a matrix; step two, initializing population; step three, obtaining individual optimum and global optimum; step four, generating new individuals; step five, generating new individual optimum and global optimum; step six, judging whether the current iteration meets the maximum iteration or not, if yes, performing the step seven, if not, returning to the step four; step seven, performing image segmentation; and step eight, outputting segmented image matrix. The Monte Carlo method is used for multiple measurement during image segmentation threshold valuating, cooperation strategy is used for individuals obtained by multiple measurement, and accordingly the medical image segmentation method has the advantage of quickness in obtaining of ideal segmentation results and can be used for multi-threshold segmentation of medical images.