A measure for registration of medical images that currently draws much attention is mutual information. The measure originates from information theory, but has been shown to be successful for image registration as well. Information theory, however, offers many more measures that may be suitable for image registration. These all measure the divergence of the joint distribution of the images' grey values from the joint distribution that would have been found had the images been completely independent. This paper compares the performance of mutual information as a registration measure with that of other F-information measures. The measures are applied to rigid registration of positron emission tomography (PET)/magnetic resonance (MR) and MR/computed tomography (CT) images, for 35 and 41 image pairs, respectively. An accurate gold standard transformation is available for the images, based on implanted markers. The registration performance, robustness and accuracy of the measures are studied. Some of the measures are shown to perform poorly on all aspects. The majority of measures produces results similar to those of mutual information. An important finding, however, is that several measures, although slightly more difficult to optimize, can potentially yield significantly more accurate results than mutual information.

A method for localization and segmentation of bifurcated aortic endografts in computed tomographic angiography (CTA) images is presented. The graft position is determined by detecting radiopaque markers sewn on the outside of the graft. The user indicates the first and the last marker, whereupon the remaining markers are automatically detected. This is achieved by first detecting marker-like structures through second-order scaled derivative analysis, which is combined with prior knowledge of graft shape and marker configuration. The identified marker centers approximate the graft sides and, derived from these, the central axis. The graft boundary is determined by maximizing the local gradient in the radial direction along a deformable contour passing through both sides. Three segmentation methods were tested. The first performs graft contour detection in the initial CT-slices, the second in slices that were reformatted to be orthogonal to the approximated graft axis, and the third uses the segmentation from the second method to find a more reliable approximation of the axis and subsequently performs contour detection. The methods have been applied to ten CTA images and the results were compared to manual marker indication by one observer and region growing aided segmentation by three observers. Out of a total of 266 markers, 262 were detected. Adequate approximations of the graft sides were obtained in all cases. The best segmentation results were obtained using a second iteration orthogonal to the axis determined from the first segmentation, yielding an average relative volume of overlap with the expert segmentations of 92%, while the interexpert reproducibility is 95%. The averaged difference in volume measured by the automated method and by the experts equals the difference among the experts: 3.5%.

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations. Results of combining both standard mutual information as well as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.

Ridge-like structures in digital images may be extracted by convolving the images with derivatives of Gaussians. The choice of the convolution operator and of the parameters involved defines a specific ridge image. In this paper, various ridge measures related to isophote curvature are constructed, reviewed, and evaluated with respect to their usability in CT/MRI matching of human brain scans. Construction is initially done using heuristics in two-dimensional images, and then established firmly in a mathematical framework. Attention is paid to the necessity of operator invariance, scale of the operator, extension to three-dimensional images, and relations to isophote and principal curvature. It is shown that one of the ridge measures appears well suited for the purpose of matching, despite the fact that the measure fails to detect ridges in a number of stylized scenes.

Describes an automated approach to register CT and MR brain images. Differential operators in scale space are applied to each type of image data, so as to produce feature images depicting "ridgeness". The resulting CT and MR feature images show similarities which can be used for matching. No segmentation is needed and the method is devoid of human interaction. The matching is accomplished by hierarchical correlation techniques. Results of 2-D and 3-D matching experiments are presented. The correlation function ensures an accurate match even if the scanned volumes to be matched do not completely overlap, or if some of the features in the images are not similar.