Reducing Interpolation Artifacts for Mutual Information Based Image Registration

Medical image registration methods which use mutual information as similarity measure have been improved in recent decades. mutual information is a basic concept of information theory which indicates the dependency of two random variables (or two images). in order to evaluate the mutual information of two images, their joint probability distribution is required. several interpolation methods, such as partial volume (pv) and bilinear, are used to estimate joint probability distribution. both of these two methods yield some artifacts on mutual information function. hanning window? partial volume and generalized partial volume methods are introduced to remove such artifacts. in this paper, we show that the acceptable performance of these methods is not due to their kernel function. it’s because of the number of pixels which incorporate in interpolation. since using more pixels requires more complex and time consuming interpolation process, we propose a new interpolation method which uses only four pixels (the same as pv and bilinear interpolations) and removes most of the artifacts. experimental results of the registration of computed tomography images show superiority of the proposed scheme.