For addressing prematurity and initial sensitive problems with traditional fuzzy clustering, artificial immune
algorithm is utilized for optimizing fuzzy clustering image segment, which has excellent ability on exploration and
exploitation. A new method for antibody density estimation is proposed based on improved Hausdorff distance, and
corresponding immune operators are defined. A new validity index function is selected as fitness function. The number
and centers of clusters are adaptively decided by searching optimization using artificial immune algorithm, which realizes
automatic image segment. Simulation results show that proposed algorithm can automatically segment image with high
validity.