A survey of image registration techniques
ACM Computing Surveys (CSUR)
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Multimodal Medical Image Registration Using Particle Swarm Optimization
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 03
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Genetic algorithms for a robust 3-D MR-CT registration
IEEE Transactions on Information Technology in Biomedicine
Bio-inspired multi-agent systems for reconfigurable manufacturing systems
Engineering Applications of Artificial Intelligence
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In image guided surgery, the registration of pre- and intraoperative 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.