Frederik Maes

Bio: Frederik Maes is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Image registration & Image segmentation. The author has an hindex of 59, co-authored 398 publications receiving 22377 citations. Previous affiliations of Frederik Maes include The Catholic University of America & Catholic University of Leuven.

Multimodality image registration by maximization of mutual information

Abstract: A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI 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 MI 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 MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (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 preprocessing steps which makes this method very well suited for clinical applications.

Automated model-based tissue classification of MR images of the brain

Abstract: Describes a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multi-spectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. The authors have validated the technique on simulated as well as on real MR images of the brain.

Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

Abstract: Comparison and evaluation of retrospective intermodality brain image registration techniques

Multi-modality image registration by maximization of mutual information

Abstract: Mutual information of image intensities has been proposed as a new matching criterion for automated multi-modality image registration. In this paper the authors give experimental evidence of the power and the generality of the mutual information criterion by showing results for various applications involving CT, MR and PET images. The authors' results illustrate the large applicability of the approach and demonstrate its high suitability for routine use in clinical practice.