Petra A. van den Elsen

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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(More)
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(More)
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(More)
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(More)
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(More)
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(More)
We present a method to correct the geometric distortion caused by field inhomogeneity in MR images of patients wearing MR-compatible stereotaxic frames. Our previously published distortion correction method derives patient-dependent error maps by computing the phase-difference of 3D images acquired at different TEs. The time difference (delta TE = 4.9 ms at(More)
Geometrical image features like edges and ridges in digital images may be extracted by convolving the images with appropriate derivatives of Gaussians. The choice of the convolution operator and of the parameters of the Gaussian involved deenes a speciic feature image. In this paper, various feature images derived from CT and MR brain images are deened and(More)
In this paper we present techniques for frameless registration of 3D Magnetic Resonance (MR) and Computed Tomography (CT) volumetric data of the head and spine. We present techniques for estimating a 3D affine or rigid transform which can be used to resample the CT (or MR) data to align with the MR (or CT) data. Our technique transforms the MR and CT data(More)
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 chapter to construct operators that extract similar structures from these images that will enable registration by simple grey value based methods, such as optimization of cross-correlation. These operators can(More)