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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. In this paper, we propose a new approach using particle swarm optimization (PSO) for medical image registrations. Particle swarm optimization is a stochastic, population-based evolutionary computer algorithm. The effectiveness of PSO has been demonstrated for both rigid and non-rigid medical image registration.