In this paper we present a system that combines the benefits of 3D deformable models and level set methods for medical volume segmentation. Our 3D deformable model is a very computationally efficient method for segmenting medical volumes, however it is not currently able to segment features, such as renal arteries, that are small relative to the imaging slice thickness used. Level Set methods are an alternative approach to deformable models that re-pose the volume segmentation problem as the calculation of the steady state of an initial value Partial Differential Equation (PDE) system on a regular rectilinear or cubic mesh. The segmentation obtained is parameterised by the zero value level set of this mesh (analogous to an iso-surface). These methods are very computationally expensive, but have the advantage of being able to segment relatively small features such as renal arteries. The problem domain explored in this paper is the segmentation of arterial structures. The results of these segmentations are to be used in the assessment of patient suitability for minimally invasive (keyhole) surgical procedures in patients with abnormal aortic aneurysms. An abdominal aortic aneurysm (AAA) is a dilation of the abdominal aorta. AAAs usually increase in size with time, and if left untreated eventually rupture causing catastrophic haemorrhage. An AAA may be treated by conventional surgical methods, but increasingly minimally invasive techniques, where a stent graft is placed in the lumen, are being used. Patient suitability is assessed using CT data and a calibrated projection angiogram - only about 10% of patients are suitable for the keyhole repair. Once a candidate has been assessed as suitable, measurements are made from the same images to determine the key dimensions of the required stent. Our overall aim is to automate both of these stages of image analysis, ensuring that the full 3D nature of the CT is used. In this paper we describe a segmentation aproach that combines the benefits of a 3D deformable model

Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (correspondence by sensitivity to movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondences.

OBJECTIVE: : To develop a generic methodology for the online assessment of medical education materials available on the World Wide Web and to implement it for pilot subject areas. DESIGN: An online questionnaire was developed, based on an existing scheme for computer-based learning material. It was extended to involve five stages, covering general suitability, local suitability, the user interface, educational style, and a general review. It is available on the Web, so expert reviewers may be recruited from outside the home institution. The methodology was piloted in three subjects areas--clinical chemistry, radiology, and medical physics--concentrating initially on undergraduate teaching. MEASUREMENTS: The contents of completed questionnaires were stored in an offline database. Selected fields, likely to be of use to students and educators searching for material, were input into an online database. RESULTS: The online assessment was used successfully in clinical chemistry and medical physics but less well in radiology. Fewer resources were found to fit local needs than expected. CONCLUSION: The methodology was found to work well for topics where teaching is highly structured and formal and is potentially applicable in other such disciplines. The approach produces more structured and applicable lists of resources than can be obtained from search engines.

In many applications in computer vision and signal processing, it is necessary to assimilate data from multiple sources. This is a particularly important issue in medical imaging, where information on a patient may be available from a number of different modalities. As a result, there has been much recent research interest in this area. The authors suggest an additional Bayesian method which generates a segmented classification concurrently with improving reconstructions of a set of registered images. A synthetic example is used to demonstrate the subjectives and benefits of this proposed approach. Two medical applications, one fusing computed tomography (CT) and single photon emission computed tomography (SPECT) brain scans, and the other magnetic resonance (MR) images at two different resolutions, are considered.