Abstract—In this paper, we describe an extrinsic-point-based, interactive image-guided neurosurgical system designed at Vanderbilt University, Nashville, TN, as part of a collaborative effort among the Departments of Neurological Surgery, Computer Science,

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

Abstract — Precise and accurate models of the world are critical to autonomous robot operation. Just as robot navigation typically requires an accurate map of the world, robot manipulation typically requires accurate models of the objects to be grasped. However, the statistical inference tools that enable robot mapping have not yet had the same impact in geometric object modelling. We describe an inference algorithm for learning statistical models of objects from image data. We describe a representation that allows us to compute a distribution over the complete geometry of different objects, and describe how a library of object geometry models can be learned. Finally, we describe how learned object models can both be used to recognize new instances of objects and to infer the geometry of occluded parts of objects. I.

Abstract: Providing formative feedback is a time-consuming activity for tutors. In the framework of the European funded projects TENCompetence and LTfLL ("Language Technologies for Lifelong Learning") we are currently exploring several methods to provide formative feedback using a comparison between expert semantic networks, based on course materials and tutor notes, and networks generated from student text materials. In this paper we explain the rationale behind this approach, give a short overview about possible implementations and discuss a validation scenario with medical students of the University of Manchester. 1

This paper presents discussion of an integrated method of multidimensional scaling (MDS) and Procrustes transformation to visualize a large amount of clustered data. Multidimensional scaling is a powerful tool that represents the relationships of objects. Cox and Cox (2001) summarized several techniques of MDS. Procrustes analysis seeks the isotropic dilation and the rigid translation, reflection, and rotation that are needed to best

Guidance systems designed for neurosurgery, hip surgery, spine surgery, and for approaches to other anatomy that is relatively rigid can use rigid-body transformations to accomplish image registration. These systems often rely on point-based registration to determine the transformation, and many such systems use attached fiducial markers to establish accurate fiducial points for the registration, the points being established by some fiducial localization process. Accuracy is important to these systems, as is knowledge of the level of that accuracy. An advantage of marker-based systems, particularly those in which the markers are bone-implanted, is that registration error depends only on the fiducial localization error (FLE) and is thus to a large extent independent of the particular object being registered. Thus, it should be possible to predict the clinical accuracy of marker-based systems on the basis of experimental measurements made with phantoms or previous patients. This paper pr...

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Abstract. To register two images means to align them so that common features overlap and differences — for example, a tumor that has grown — are readily apparent. Being able to easily spot differences between two images is obviously very important in applications. This paper is an introduction to image registration as applied to medical imaging. We first define image registration, breaking the problem down into its constituent component. We then discuss various techniques, reflecting different choices that can be made in developing an image registration technique. We conclude with a brief discussion. 1.

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