Petra A. van den Elsen

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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)
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)
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)
Surface (e.g., skin or cortex) based methods to register SPECT and MR images are well known and widely used. Such methods have the disadvantage of needing some kind of segmentation to obtain the surface, which is often a high-level task and possibly error-prone. Also, when reducing the grey-valued images to surfaces, i.e., binary structures, valuable(More)
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 speciic derivatives involved are based on the local gradient and second order structure. The width of the used Gaussian determines the locality of(More)