Hybrid Particle Swarm Optimization for Medical Image Registration

Medical image registration is an important issue. In registrations, we seek an estimate of the transformation that registers the reference image and test image by optimizing their metric function (similarity measure). To date, local optimization techniques, such as the gradient decent method, are frequently used for medical image registrations. But these methods need good initial values for estimation in order to avoid the local minimum. In this paper, we propose a new approach named hybrid particle swarm optimization (HPSO) for medical image registration, which incorporates two concepts (subpopulation and crossover) of genetic algorithms into the conventional PSO. Experimental results with medical volume phantom data show that the proposed HPSO performs much better results than conventional GA and PSO.