Automatic registration of multimodality images is an upcoming field o f research for medical diagnosis and surgical planning. We propose a new method to extract com mon features from different modalities based on the extension to 3D of Rothe’s creases. Extracted images ar e used as input for a multiresolution search algorithm which brings them into agreement.

This paper addresses the registration of ultrasound scans and magnetic resonance (MR) volume datasets of the same patient. During a neurosurgery intervention, pre–operative MR images are often employed as a guide despite the fact that they do not show the actual state of the brain, which sometimes has sunk up to 1 cm. By means of a standard ecographer and a tracker connected to a computer, it is feasible to build on-line an updated picture of the brain. We propose an algorithm which first composes the volume ecography of the brain and registers it to the MR volume. Next, it aligns individual B-scans into the MR volume, thus providing a measure of the suffered deformation.

Ultrasound imaging is a helpful aid for diagnosis in many medical specialities. In order to enhance the information available to the examiner an upcoming approach is used to compound the sequence of video images into a single 3D volume, which needs a real-time tracking of the transducer However, errors in the positioning occur and the compounded image becomes degraded. We propose a method which automatically corrects the positioning error by means of a registration step between the live echography and compounded volume. The algorithm was tested on sequences taken on an in-vitro human brain.

Ridges and valleys are Earth's relief structures. They can have an imaging counterpart provided we model a digital image as a landscape by considering grey level values as height. These two dual entities have received comparatively little attention from the computer vision community with regard to other like edges or comets. In this paper, we first propose a taxonomy or classification of the several definitions of ridge and valley lines and review their implementation on discrete images. Then, we illustrate the application of one type of characterization, a creaseness measure, to solve several problems of image registration, where ridge and valley lines are taken as landmarks.

Ridges and valleys are earth's relief structures. They can have an imaging counterpart provided we model a digital image as a landscape by considering grey level values as height. These two dual entities have received comparatively little attention from the computer vision community with regard to others like edges or corners. In this paper, we first propose a taxonomy or classification of the several definitions of ridge and valley lines and review their implementation on discrete images. Then, we illustrate the application of one type of characterization, a creaseness measure, to solve several problems of image registration, where ridge and valley lines are taken as landmarks.

Creases are a type of ridge/valley structures of an image characterized by local conditions. As creases tend to be at the center of anisotropic grey-level shapes, creaseness can be considered a measure of medialness, and therefore as useful in many image analysis problems. Among the several possibilities, a priori the creaseness based on the level-set extrinsic curvature (LSEC) is especially interesting due to its invariance properties. However, in practice, it produces a discontinuous response with a badly dynamic range. The same problems arise with other related creaseness measures proposed in the literature. In this paper, we argue that these problems are due to the very local definition of the LSEC. Therefore, rather than designing an ad hoc solution, we propose two new multilocal creaseness measures that we will show to be free of discontinuities and to have a meaningful dynamic range of response. Still, these measures are based on the LSEC idea, to preserve its invariance properties. We demonstrate the usefulness of the new creaseness measures in the context of two applications that we are currently developing in the field of 3D medical image analysis, the rigid registration of CT and MR head volumes and the orientation analysis of trabecular bone patterns.

Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized

This paper describes a method which uses the skull as a landmark for automatic registration of computer tomography to magnetic resonance (MR) images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and robustness against the well-known mutual information method for five different pairs of images. We have found both comparable, and for certain MR images the proposed method achieves better performance. (C) 1999 SPIE and IS&T. [S1017-9909(99)00403-1].

All image-guided neurosurgical systems that the authors are aware of assume that the head and its contents behave as a rigid body. It is important to measure intraoperative brain deformation (brain shift) to provide some indication of the application accuracy of image-guided surgical systems, and also to provide data to develop and validate nonrigid registration algorithms to correct for such deformation. The authors are collecting data from patients undergoing neurosurgery in a high-field (1.5 T) interventional magnetic resonance (MR) scanner. High-contrast and high-resolution gradient-echo MR image volumes are collected immediately prior to surgery, during surgery, and at the end of surgery, with the patient intubated and lying on the operating table in the operative position. Here, the authors report initial results from six patients: one freehand biopsy, one stereotactic functional procedure, and four resections. The authors investigate intraoperative brain deformation by examining threshold boundary overlays and difference images and by measuring ventricular volume. They also present preliminary results obtained using a nonrigid registration algorithm to quantify deformation. They found that some cases had much greater deformation than others, and also that, regardless of the procedure, there was very little deformation of the midline, the tentorium, the hemisphere contralateral to the procedure, and ipsilateral structures except those that are within 1 cm of the lesion or are gravitationally above the surgical site.

Image registration or matching attempts to solve the problem that arises when two images taken at different times, by different sensors or from different viewpoints need to be compared. An upcoming application of image registration is in the field of medical images, specially since the introduction of 3-D modalities. Many methods have been proposed for multi-sensor medical image registration [1]. Our research has focused in CT-MR registration because these modalities are widely available and they provide complementary information: CT depicts accurately bones, while MR distinguishes soft tissues.

Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature k/sub M/ of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy.