Abstract-A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, Mutual Information or relative entropy, as a new matching criterion. The method presented in this paper applies Mutual Information to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of Mutual Information is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the mutual information criterion is validated for rigid body registration of CT, MR and PET images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction or other pre-processing steps, which makes this method very well suited for clinical applications.

Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Mutual information of image intensities has been proposed as a iiew matching criterion for automated multimodality image registration. In this papel; we give experimental evidence of the power and the generality of the mutual information criterion by showing results for various applications involving C7: MR and PET images. Our results illustrate the large applicability of the approach and demonstrate its high suitability for routine use in clinical practice.

Abstract. In this paper, 3D voxel-similarity-based VB registration algorithms that optimize a feature-space clustering measure are proposed to combine the segmentation and registration process. We present a unifying de nition and a classi cation scheme for existing VB matching criteria and propose a new matching criterion: the entropy of the grey-level scatter-plot. This criterion requires no segmentation or feature extraction and no a priori knowledge of photometric model parameters. The effects of practical implementation issues concerning grey-level resampling, scatter-plot binning, parzen-windowing and resampling frequencies are discussed in detail and evaluated using real world data CT and MRI .

Abstract. In this paper, 3D voxel-similarity-based VB registration algorithms that optimize a feature-space clustering measure are proposed to combine the segmentation and registration process. We present a unifying de nition and a classi cation scheme for existing VB matching criteria and propose a new matching criterion: the entropy of the grey-level scatter-plot. This criterion requires no segmentation or feature extraction and no a priori knowledge of photometric model parameters. The effects of practical implementation issues concerning grey-level resampling, scatter-plot binning, parzen-windowing and resampling frequencies are discussed in detail and evaluated using real world data CT and MRI.