Hybrid particle swarm optimization for 3-D image registration
Yen‐Wei Chen, Aya Mimori, Chen-Lun Lin · 2009
In image guided surgery, the registration of pre-and intra-operative image data 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. Recently several global optimization methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have been proposed for medical image registration. In this paper, we propose a new approach named hybrid particle swarm optimization (HPSO) for 3-D medical image registration, which incorporates two concepts (subpopulation and crossover) of genetic algorithms into the conventional PSO. Experimental results with both mathematic test functions and medical volume data show that the proposed HPSO performs much better results than conventional gradient decent method, GA and PSO.