Multimodality image registration by maximization of mutual information

Abstract

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.

Unifying the analyses of anatomical and diffusion tensor images using volume‐preserved warping

“…To our knowledge, however, no method for multimodal imaging has yet been developed that simultaneously addresses the challenges of both coregistration and ROI delineation. Some groups, for example, have proposed elegant deformation algorithms that improve the quality of cross-modal coregistration (13)(14)(15)(16)(17)(18)(19)(20)(21), and others have used existing software packages for cross-modal coregistration, such as statistical parametric mapping (SPM) (18,19) and automated image registration (AIR) (20,21). These methods, however, have not been integrated with methods for the automated delineation of ROIs as a basis for fiber tracking.…”

Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets

“…global linear registration (12 degrees of freedom) to the standard image [21], maximizing mutual information between the two volumes [22];…”

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

“…Additionally, Doppler ultrasound images inherently contain a lot of (speckle) noise. To register this kind of images it is necessary to find a similarity measure that does not make any assumptions regarding the nature of the relation between the image intensities (see also [21] and [19]). …”

“…Since various information from image data is exploited to drive the image registration algorithms, we can classify registration algorithms according to the information content used in registration into algorithms using designated landmarks [14,15], contours [16] and surfaces [17] or various pixel properties functions [18]. The method proposed in this paper is based on the normalized mutual information (NMI) image similarity criteria [19,20,21] and a specially formulated geometrical transformation.…”