Image registration using mutual information

Abstract

Different imaging modalities, such as CT, MRI and PET, are based on different physical principles and capture different and often complementary information. Many applications in clinical practice benefit from an integrated visualization and combined analysis of such multimodal images. In many applications it is also necessary to compare images acquired at a different time points, such as in the analysis of dynamic image sequences or of follow-up studies. Analysis of a single scene from multiple images assumes that the geometrical correspondence or registration between these images is known, such that anatomically identical points can be precisely identified and compared in each of the images. But reliable automated retrospective fusion or registration of multimodality images based on intrinsic image features is complicated by their different photometric properties, by the complexity of the scene and by the large variety of clinical applications. Maximization of mutual information of corresponding voxel intensities allows for fully automated registration of multimodality images without need for segmentation or user intervention, which makes it well suited for routine clinical use in a variety of applications.