André Collignon

Multimodality image registration by maximization of mutual information

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.

3D Multi-Modality Medical Image Registration Using Feature Space Clustering

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 definition and a classification 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).

Automatic registration of 3D images of the brain based on fuzzy objects

Multimodal fuzzy voxel labeling is presented as the basis for a new image registration criterion. The corresponding registration systems architecture performs an iterative calculation of the labeling and the registration process simultaneously, while most other registration systems perform segmentation and iterative estimation of registration parameters sequentially. It will be argued that its application leads to more automated and more accurate registration solutions than does e.g. the use of typical surface based registration systems. In order to support the arguments raised we have performed a case study using both 2D MR software phantom image, and 2D and 3D MR/CT image data. In this case study we looked at the behaviour of maximum likelihood voxel labeling as the simplest instantiation of a fuzzy voxel labeling algorithm. However, the architecture is open to integration of more general multimodal fuzzy voxel labeling algorithms.

New high-performance 3D registration algorithms for 3D medical images

In this presentation a new search method is proposed to improve the speed and accuracy of surface based registration algorithms. Furthermore, a parallel point projection based, multicomponent distance evaluation method is presented. This method offers an elegant solution to the problem of partially overlapping data sets. An adaptive outlier treatment method is also presented. Combination of all these new techniques results in a faster surface based algorithm with better accuracy, but above all with better reliability then existing surface based 3D registration algorithms. In the context of the surface correspondence problem, surface based registration algorithms are compared to feature matching methods.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An Object Oriented Tool for 3D Multimodality Surface-based Image Registration

Three surface-based registration algorithms using different point projection-based matching criteria and using different distance minimization strategies are analyzed: Pelizzari’s head-hat algorithm, a variant of Borgefors’s hierarchical chamfer matching, and Besl and McKay’s iterative closest point algorithm. This work is part of COVIRA (Computer Vision in Radiology), project A2003 of the AIM (Advanced Informatics in Medicine) programme of the European Commission. COVIRA-specific object oriented aspects of the algorithms’ implementations are briefly discussed. Finally they are compared with respect to speed, accuracy and flexibility. This comparison is based on the registration results of the COVIRA reference data sets (MRI/MRA and MR/CT), and a database of MR/PET data sets.