Daniel B. Russakoff, Carlo Tomasi, Torsten Rohlfing and Calvin R. Maurer Jr.,
Image Similarity Using Mutual Information of Regions,
In Proceedings of the Eighth European Conference on Computer
Vision (ECCV 2004),
Prague, Czech Republic, pp. 596-607, May 11-14 2004.Mutual information (MI) has emerged in recent years as an effective similarity measure for comparing images. One drawback of MI, however, is that it is calculated on a pixel by pixel basis, meaning that it takes into account only the relationships between corresponding individual pixels and not those of each pixels respective neighborhood. As a result, much of the spatial information inherent in images is not utilized. In this paper, we propose a novel extension to MI called regional mutual information (RMI). This extension efficiently takes neighborhood regions of corresponding pixels into account. We demonstrate the usefulness of RMI by applying it to a real-world problem in the medical domain�intensity-based 2D-3D registration of X-ray projection images (2D) to a CT image (3D). Using a gold-standard spine image data set, we show that RMI is a more robust similarity meaure for image registration than MI.