The affection of grey levels on mutual information based medical image registration

Medical image registration is an active research field in medical image processing in the last two decades. One of the surveys of medical image registration is voxel-based method, which is generally considered as very accurate, almost fully automatic. The most efficient voxel-based method is maximization of mutual information (MI). In this paper, we will discuss the effects of rescaling intensity values of an image on MI method, and then compare the registration results and computation time of the same image pairs with different grey levels. We implemented series rigid registration of CT and MR image, in each registration, the intensity values of CT and MR images are linearly reseated to the same range. We adopt 5 different levels in our experiments, i.e. 256, 128. 64, 32 and 16 grey levels to exploit the affection of grey levels on MI based registration method. We also count the time cost on the calculation of single MI value and the whole registration procedure. Finally we reach this conclusion that the results become worse with the reduction of grey levels of the image, and rescaling the intensity values of the original images into [0,63] is an excellent tradeoff in brain registration by mutual information.