Images from computed tomography (CT), magnetic resonance (MR) imaging, positron emission tomography (PET), and single photon emission computed tomography (SPECT), etc., provide complementary characteristic and diagnostic information. A parametric Chamfer Matching method is used for fast and accurate registration of the images from different medical imaging modalities. Surfaces are initially extracted from two images to be matched using semi-automatic segmentation software, and then these surfaces are used as common features to be matched. A distance transformation is performed for one surface image, and an error function is developed using the distance-image to evaluate the matching error. The geometric transformation includes three-dimensional translation, rotation, and scaling parameters to accommodate images of different position, orientation, and size. The matching process involves searching the multi-parameter space to find the fit which will minimize the error function. The local minima problem is addressed by using a large number of starting points. A pyramid multiresolution approach is employed to speed up both the distance transformation and the multi-parameter minimization processes. Robustness in handling noise is enhanced by using multiple thresholds approach imbedded in the multi-resolution process. Human intervention is not necessary.