A multiscale approach to mutual information matching

Methods based on mutual information have shown promising results for matching of multimodal brain images. This paper discusses a multiscale approach to mutual information matching, aiming for an acceleration of the matching process while considering the accuracy and robustness of the method. Scaling of the images is done by equidistant sampling. Rigid matching of 3D magnetic resonance (MR) and computed tomography (CT) brain images is performed on datasets of varying resolution and quality. The experiments show that a multiscale approach to mutual information matching is an appropriate method for images of high resolution and quality. For such images an acceleration up to a factor of around 3 can be achieved. For images of poorer quality caution is advised with respect to the multiscale method, since the optimisation method used (Powell) was shown to be highly sensitive to the local optima occurring in these cases. When incorrect intermediate results are avoided, an acceleration up to a factor of around 2 can be achieved for images of lower resolution.