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. Index Terms—Matching criterion, multimodality images, mutual information, registration. I.

The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. Keywords: registration, matching Received May 25, 1997

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strategies based image registration via feature matching

Methods based on mutual information have shown promising results for matching of multimodal brain images. This paper discusses a multiscale approach to mutual information matching, aiming for an acceleration of the matching process while considering the accuracy and robustness of the method. Scaling of the images is done by equidistant sampling. Rigid matching of 3D magnetic resonance (MR) and computed tomngraphy (CT) brain images is performed on datasets of varying resolution and quality. The experiments show that a multiscale approach to mutual information matching is an appropriate method for images of high resolution and quality. For such images an acceleration up to a factor of around 3 can be achieved. For images of poorer quality caution is advised with respect to the multiscale method, since the optimisation method used (Powell) was shown to be highly sensitive to the local optima occurring in these cases. When incorrect intermediate results are avoided, an acceleration up to a factor of around 2 can be achieved for images of lower resolution.

In this paper, we will show the feasibility of using ridgeness for rigid automatic matching of CT and MR brain images. Image ridgeness can be computed by convolving the image with derivatives of Gaussians. The speci c derivatives involved are based on the local gradient and second order structure. The width of the used Gaussian determines the locality of the ridgeness computed. 1

put of the detector plane of (c) is shown in (e). The entire surface is smoother than (d). The uncorrupted corner and the blurred feature give a less pronounced peak; the position of the corrupted corner cannot be detected with confidence and several likely locations are indicated by the smooth hill. Thus, detection and placement can be improved by using sharp feature representations. The aim of this chapter is to develop feature sets with sharp contours. Three amendments to the previously proposed architecture are proposed: the use of spatial competition during training is outlined in x6.2, the selection of a subset of features from a larger set is suggested in x6.3, and the application of threshold-like, feature post-processing is discussed in x6.4. First a description of the three methods is given which is followed by an experimental investigation in x6.5. The new feature types of the three methods are given in

This work addresses the distortions in Functional Magnetic Resonance Images (FMRI) caused by subject motion. FMRI is a non-invasive technique which shows great promise in providing researchers and clinicians with neurological information both about healthy subjects and clinical patients by mapping functional activation within the brain using Echo Planar Imaging (EPI). If reliable information is to be obtained from these images, motion correction must be carried out in order to remove or suppress the artefacts arising from subject movement. This work begins by using exploratory data techniques to describe these artefacts so that they can be characterised according to their origin and spatio-temporal manifestation. Based on testing of the accuracy and consistency of existing rigid-body motion correction methods on FMRI data, a new registration algorithm — Motion Correction using the FMRIB Linear Image Registration Tool (MCFLIRT) — has been developed. It is shown that while MCFLIRT is both more accurate and more

In this paper we present a novel technique for non-rigid medical image registration and correspondence finding based on a multiple-layer flexible mesh template matching technique. A statistical anatomical model is built in the form of a tetrahedral mesh, which incorporates both shape and density properties of the anatomical structure. After the affine transformation and global deformation of the model are computed by optimizing an energy function, a multiple-layer flexible mesh template matching is applied to find the vertex correspondence and achieve local deformation. The multiple-layer structure of the template can be used to describe different scale of anatomical features; furthermore, the template matching is flexible which makes the correspondence finding robust. A leave-one-out validation has been conducted to demonstrate the effectiveness and accuracy of our method. Keyword: Non-rigid registration, statistical model, multiple-layer flexible mesh template, correspondence 1. Background and introduction Non-rigid medical image registration is an essential step in many automated medical image

Facial animation and cranio-facial surgery simulation both stand to benefit from the development of anatomically accurate computer models of the human face. State-of-theart biomechanical models of the face have shown promise in animation, but they are inadequate for the purposes of cranio-facial surgery simulation. The goal of this thesis is to develop an improved facial model, using Cyberware data which captures the external structure and appearance of the face and head, as well as computed tomography (CT) data which captures the internal structure of facial soft and hard tissues. To this end, we develop algorithms to (1) register the CT and Cyberware datasets, (2) extract from the CT data a skull subsurface which serves as a foundation of the soft-tissue model, and (3) compute thic...