In order to improve the efficiency of image segmentation, Quantum-behaved Particle Swarm Optimization (QPSO ) algorithm was used to image threshold segmentation, and BQPSO, an improved threshold searching algorithm based on QPSO, was proposed. BQPSO algorithm introduced a boundary-controlled strategy to reset particles back to a random point around the border in search region when particles were massed on border, so to prevent them from aggregating at the border. By Boundary-controlled strategy, a diversity of the swarm was maintained , local optimal solution was avoided efficiently, and the global search ability was enhanced. The experiment result showed that, compared with GA,PSO and QPSO, BQPSO algorithm possess obviously advantage in threshold searching efficiency, searching accuracy and image segmentation effect.
