There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Improved optimization for the robust and accurate linear registration and motion correction of brain images

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://www.fil.ion.ucl.ac.uk/~karl/Voxel-Based%20Morphometry.pdf

Voxel-Based Morphometry—The Methods

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.

/pdf/multimodality-image-registration-by-maximization-of-mutual-1q65lsx4nm.pdf

Multimodality image registration by maximization of mutual information

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.

/pdf/automated-model-based-tissue-classification-of-mr-images-of-2l1ptkjz64.pdf

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

Comparison and evaluation of retrospective intermodality brain image registration techniques

/pdf/comparison-and-evaluation-of-retrospective-intermodality-2y5r9x7okq.pdf

Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

Automated multi-moda lity image registration based on information theory

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.

Dirk Vandermeulen

Papers

Multimodality image registration by maximization of mutual information

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

Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques

Automated multi-moda lity image registration based on information theory

Multi-modality image registration by maximization of mutual information