Petra A. van den Elsen

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.

Grey value correlation techniques used for automatic matching of CT and MR brain and spine images

Grey value correlation is generally considered not to be applicable to matching of images of different modalities. In this paper we will demonstrate that, with a simple preprocessing step for the Computed Tomography (CT) images, grey value correlation can be used for matching of Magnetic Resonance Imaging (MRI) with CT images. Two simple schemes are presented for automated 3D matching of MRI and CT neuroradiological images. Both schemes involve grey value correlation of the images in order to determine the matching transformation. In both schemes the preprocessing consists of a simple intensity mapping of the original CT image only. It will be shown that the results are insensitive to considerable changes in the parameters that determine the intensity mapping. Whichever preprocessing step is chosen, the correlation method is robust and accurate. Results, compared with a skin marker-based matching technique, are shown for brain images. Additionally, results are shown for an entirely new application: matching of the cervical spine.

Image fusion using geometrical features

This paper describes a new approach to register images obtained from different modalities. Differential operators in scale space are used to extract geometric features from the images corresponding to similar structures. The resulting feature images may be matched by minimizing some function of the distances between the features in the respective images. Our first application concerns matching of brain images. We discuss a differential operator that produces ridge-like feature images from which the center curve of the cranium is easily extracted in CT and MRI. Results of the performance of these operators in 2-D matching tasks are presented. In addition, the potential of this approach for multimodality matching of 3-D medical images is illustrated by the striking similarity of the ridge images extracted from CT and MR images by the 3-D version of the operator.

Registration error quantification of a surface‐based multimodality image fusion system

This paper presents a new reference data set and associated quantification methodology to assess the accuracy of registration of computerized tomography (CT) and magnetic-resonance (MR) images. Also described is a new semiautomatic surface-based system for registering and visualizing CT and MR images. The registration error of the system was determined using a reference data set that was obtained from a cadaver in which rigid fiducial tubes were inserted prior to imaging. Registration error was measured as the distance between an analytic expression for each fiducial tube in one image set and transformed samples of the corresponding tube obtained from the other. Registration was accomplished by first identifying surfaces of similar anatomic structures in each image set. A transformation that best registered these structures was determined using a nonlinear optimization procedure. Even though the root-mean-square (rms) distance at the registered surfaces was similar to that reported by other groups, it was found that rms distances for the tubes were significantly larger than the final rms distances between the registered surfaces. It was also found that minimizing rms distance at the surface did not minimize rms distance for the tubes.

Accurate Matching of electromagnetic dipole data with CT and MR images

SummaryInterpretation of EEG (electroencephalography) or MEG (magnetoencephalography) derived three-dimensional dipole localizations is hampered by poor visualization. This paper describes a method for combining dipole data with structural image data of the same patient. To ensure high precision this method utilizes external markers that are easy to apply. These markers can achieve subslice accuracy and can even be used to pinpoint reference points outside the scanned volume. Accurate matching is thus provided even in standard imaging protocols employing thick slices and/or large interslice gaps. The results of the matching method are presented in 2D and 3D visualizations. The hybrid images facilitate the interpretation of dipole localizations with respect to the patients anatomy.

Integrated presentation of multimodal brain images

SummaryThis article discusses the fusion of brain images from multiple modalities as well as the presentation of the integrated image information. The paper has three parts. First, individual brain imaging modalities are compared as regards clinical appreciation, invasiveness, dimensionality, spatial resolution, temporal resolution, and cost. Next, methods to combine multiple images are briefly surveyed and collated by characteristics as accuracy, patient-friendliness, reproducibility, labour-extensiveness, feasibility of retrospective matching, and general applicability. Finally, techniques to display multimodal image information are outlined and examples of the various options for integrated presentation are shown.

Method for Correcting Magnetic Resonance Image Distortion for Frame-Based Stereotactic Surgery, with Preliminary Results

We previously described a technique for correcting patient-specific magnetic field inhomogeneity spatial distortion in magnetic resonance images (MRI), which was not applicable to patients fitted with MRI-compatible stereotactic fiducial frames. Here we describe an improvement to the technique that permits application for these patients. Measurements with a cadaver head show that this method achieves MRI stereotactic localization accuracy of 1 mm.

Geometry driven multimodality matching of brain images

SummaryClinical 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 Lυυ 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 operators performance in matching tasks are shown. Another promising operator is the ‘umbilicity’ operator, which is presented in combination with SPECT images.

A Versatile System for Multimodality Image Fusion

This paper presents a versatile system for registering and visualizing computed tomography and magnetic resonance images. The system utilizes a semi-automatic, surface-based registration strategy which has proven useful for registering a number of different anatomical structures. A triangular mesh approximates surfaces in one image set while a set of surface points is used as a surface approximation in the other set. A non-linear optimization procedure determines the transformation that minimizes the total sum-squared perpendicular distance between triangles of the mesh and surface points. This system has been used without modification to successfully register images of the brain, spine and calcaneus.

Registration of 3D Medical Images Using Simple Morphological Tools

Multimodal medical images are often of too different a nature to be registered on the basis of the image grey values only. It is the purpose of this paper to construct operators that extract similar structures from these images that will enable registration by simple grey value based methods, such as maximization of cross-correlation. These operators can be constructed using only basic morphological tools such as erosion and dilation. Simple versions of these operators are easily implemented on any computer system. We will show that accurate registration of images of various modalities (MR, CT, SPECT and PET) can be obtained using this approach.