Fuzzy C-Means algorithm is dependent on the initial value, resulting in easy to fall into the disadvantage of the local optimum value. A combination of quantum ant colony algorithm and FCM clustering algorithm is put forward. Firstly, the original center and numbers of cluster of the image are determined by using global type, robustness and advantages of fast convergence of quantum ant colony algorithm. Secondly, the obtained results are taken as the initial parameters for FCM clustering algorithm, and then the medical image is divided by using FCM clustering algorithm. It is proved that the method has reduced the dependence of FCM clustering algorithm on initial parameters effectively， overcome the shortcomings of easy falling into the local minimum of both algorithms，and greatly improved dividing speed and accuracy, which is simulated by real experiment.