Threshold selection of traditional  twodimensional Otsu algorithm generally depends on the exhaustive search method. However it cannot be applied to realtime systems for its long segmentation time and poor realtime performance which affect the efficiency of image segmentation. In order to reduce the running time of the twodimensional Otsu algorithm, we use the wolf pack algorithm to find the best threshold vector. Each artificial wolf represents a feasible twodimensional threshold vector. And the wolves get the best threshold through constant iteration of intelligent behaviors, including scouting behaviors, summoning behaviors and beleaguering behaviors, as well as the communication information among wolves. Simulation results show that compared with the twodimensional Otsu algorithm with standard PSO optimization and the traditional twodimensional Otsu algorithm, the proposed algorithm can reduce segmentation time and improve the accuracy of image segmentation.