A survey of medical image registration - under review.

A retrospective view on the past two decades of the field of medical image registration is presented, guided by the article "A survey of medical image registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic registration, the primacy of intensity-based registration, the breakthrough of nonlinear registration, the progress of inter-subject registration, and the availability of generic image registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of registration methods, and translation of results of image registration research to clinical practice. It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects.

Normalized mutual information based registration using k-means clustering and shading correction.

In this paper the influence of intensity clustering and shading correction on mutual information based image registration is studied. Instead of the generally used equidistant re-binning, we use k-means clustering in order to achieve a more natural binning of the intensity distribution. Secondly, image inhomogeneities occurring notably in MR images can have adverse effects on the registration. We use a shading correction method in order to reduce these effects. The method is validated on clinical MR, CT and PET images, as well as synthetic MR images. It is shown that by employing clustering with inhomogeneity correction the number of misregistrations is reduced without loss of accuracy thus increasing robustness as compared to the standard non-inhomogeneity corrected and equidistant binning based registration.

F-information measures in medical image registration.

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.

Mutual-information-based registration of medical images: a survey.

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.

Localization and segmentation of aortic endografts using marker detection.

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%.

Automated topographical cell proliferation analysis.

BACKGROUND: Cell proliferation is often studied using the incorporation of bromodeoxyuridine (BrdU). Immunohistochemical staining is then used to detect BrdU in the nucleus. To circumvent the observer bias and labor-intensive nature of manually counting BrdU-labeled nuclei, an automated topographical cell proliferation analysis method is developed. METHODS: Sections stained with fluorescein-labeled anti-BrdU and counterstained with To-Pro-3 are scanned using confocal laser scanning microscopy (CLSM). For every point in the image, the nucleus density of BrdU-labeled nuclei and the total nucleus density of the neighborhood of that point are calculated from the BrdU and the To-Pro-3 signal, respectively. The ratio of these densities gives an indication of the amount of cell proliferation at that point. The automated measure is validated by comparing it with the ratio of BrdU-stained nuclei to the total number of nuclei obtained from a manual count. RESULTS: A positive correlation is found between the automated measure and the ratios calculated from the manual counting (r = 0.86, P < 0.001). Calculating the topographical cell proliferation using the automated method is faster and does not suffer from interobserver variability. CONCLUSIONS: Automated topographical cell proliferation analysis is a fast method to objectively find differences in cell proliferation within a tissue. This can be visualized by a topographical map that corresponds to the tissue under study.

Registration, segmentation, and visualization of multimodal brain images.

This paper gives an overview of the studies performed at our institute over the last decade on the processing and visualization of brain images, in the context of international developments in the field. The focus is on multimodal image registration and multimodal visualization, while segmentation is touched upon as a preprocessing step for visualization. The state-of-the-art in these areas is discussed and suggestions for future research are given.

Image registration by maximization of combined mutual information and gradient information.

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.

Retrospective shading correction based on entropy minimization.

Shading is a prominent phenomenon in microscopy, manifesting itself via spurious intensity variations not present in the original scene. The elimination of shading effects is frequently necessary for subsequent image processing tasks, especially if quantitative analysis is the final goal. While most of the shading effects may be minimized by setting up the image acquisition conditions carefully and capturing additional calibration images, object-dependent shading calls for retrospective correction. In this paper a novel method for retrospective shading correction is proposed. Firstly, the image formation process and the corresponding shading effects are described by a linear image formation model, which consists of an additive and a multiplicative parametric component. Secondly, shading correction is performed by the inverse of the image formation model, whose shading components are estimated retrospectively by minimizing the entropy of the acquired images. A number of tests, performed on artificial and real microscopical images, show that this approach is efficient for a variety of differently structured images and as such may have applications in and beyond the field of microscopical imaging.

A survey of medical image registration.

The purpose of this paper is to present a survey of recent (published in 1993 or later) 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 based on either segmented points or surfaces, or on techniques endeavouring to use the full information content of the images involved.

Comparison and evaluation of retrospective intermodality brain image registration techniques.

PURPOSE: The primary objective of this study is to perform a blinded evaluation of a group of retrospective image registration techniques using as a gold standard a prospective, marker-based registration method. To ensure blindedness, all retrospective registrations were performed by participants who had no knowledge of the gold standard results until after their results had been submitted. A secondary goal of the project is to evaluate the importance of correcting geometrical distortion in MR images by comparing the retrospective registration error in the rectified images, i.e., those that have had the distortion correction applied, with that of the same images before rectification. METHOD: Image volumes of three modalities (CT, MR, and PET) were obtained from patients undergoing neurosurgery at Vanderbilt University Medical Center on whom bone-implanted fiducial markers were mounted. These volumes had all traces of the markers removed and were provided via the Internet to project collaborators outside Vanderbilt, who then performed retrospective registrations on the volumes, calculating transformations from CT to MR and/ or from PET to MR. These investigators communicated their transformations again via the Internet to Vanderbilt, where the accuracy of each registration was evaluated. In this evaluation, the accuracy is measured at multiple volumes of interest (VOIs), i.e., areas in the brain that would commonly be areas of neurological interest. A VOI is defined in the MR image and its centroid c is determined. Then, the prospective registration is used to obtain the corresponding point c' in CT or PET. To this point, the retrospective registration is then applied, producing c" in MR. Statistics are gathered on the target registration error (TRE), which is the distance between the original point c and its corresponding point c". RESULTS: This article presents statistics on the TRE calculated for each registration technique in this study and provides a brief description of each technique and an estimate of both preparation and execution time needed to perform the registration. CONCLUSION: Our results indicate that retrospective techniques have the potential to produce satisfactory results much of the time, but that visual inspection is necessary to guard against large errors.

Integration of functional and anatomical brain images.

This article concerns the integration of functional and anatomical volumetric brain images. Integration consists of two steps: matching or registration, where the images are brought into spatial agreement, and fusion or simultaneous display where the registered multimodal image information is presented in an integrated fashion. Approaches to register multiple images are divided into extrinsic methods based on artificial markers, and intrinsic matching methods based solely on the patient related image data. The various methods are compared by a number of characteristics, which leads to a clear preference for one class of intrinsic methods, viz. voxel-based matching. Furthermore, two- and three-dimensional techniques to display multimodality image information are outlined.

Comparison of edge-based and ridge-based registration of CT and MR brain images.

In modern medicine, several different imaging techniques are frequently employed in the study of a single patient. This is useful, since different images show complementary information on the functionality and/or structure of the anatomy examined. This very difference between modalities, however, complicates the problem of proper registration of the images involved, and rules out the most basic approaches--like direct grey value correlation--to achieve registration. The observation that some common structures will always exist is supportive of the statement that registration may be feasible using edges or ridges present in the images. The existence of such structures defined in the binary sense is questionable, however, and their extraction from images requires a segmentation by definition. In this paper we propose to use fuzzy edgeness and ridgeness images, thus avoiding the need for segmentation and using more of the available information from the original images. We will show that such fuzzy images can be used to achieve accurate registration. Several ridgeness and edgeness computing operators were compared. The best registration results were obtained using a gradient magnitude operator.

Geometry driven multimodality matching of brain images.

Clinical diagnosis, as well as therapy planning and evaluation, are increasingly supported by multimodal images. There are many instances desiring integration of the information obtained by various imaging devices. This paper describes a new approach to match images of different modalities. Differential operators are used in combination with Gaussian blurring to extract geometric features from the images that correspond to similar structures. The resulting 'feature' images may be used with existing matching techniques that minimize the distance between the features in the images to be matched. Our first application of this new approach concerns matching of MRI and CT brain images. The so-called L upsilon upsilon operator produces a ridge-like feature image from which in CT and MRI the center curve of the cranium is easily extracted. First results of this operator's performance in matching tasks are shown. Another promising operator is the 'umbilicity' operator, which is presented in combination with SPECT images.