Hans Christian Hege

Solving Einstein's equations on supercomputers

In 1916, Albert Einstein published his famous general theory of relativity, which contains the rules of gravity and provides the basis for modern theories of astrophysics and cosmology. For many years, physicists, astrophysicists and mathematicians have striven to develop techniques for unlocking the secrets contained in Einsteins theory of gravity; more recently, computational science research groups have added their expertise to the endeavor. Because the underlying scientific project provides such a demanding and rich system for computational science, techniques developed to solve Einsteins equations will apply immediately to a large family of scientific and engineering problems. The authors have developed a collaborative computational framework that allows remote monitoring and visualization of simulations, at the center of which lies a community code called Cactus. Many researchers in the general scientific computing community have already adopted Cactus, as have numerical relativists and astrophysicists. In June 1999, an international team of researchers at various sites ran some of the largest such simulations in numerical relativity yet undertaken, using a 256-processor SGI Origin 2000 supercomputer at the National Center for Supercomputing Applications (NCSA). Other globally distributed scientific teams are running visual simulations of Einsteins equations on the gravitational effects of colliding black holes.

Fast visualization of plane-like structures in voxel data

We present a robust, noise-resistant criterion characterizing plane-like skeletons in binary voxel objects. It is based on a distance map and the geodesic distance along the objects boundary. A parameter allows us to control the noise sensitivity. If needed, homotopy with the original object might be reconstructed in a second step, using an improved distance ordered thinning algorithm. The skeleton is analyzed to create a geometric representation for rendering. Plane-like parts are transformed into an triangulated surface not enclosing a volume by a suitable triangulation scheme. The resulting surfaces have lower triangle count than those created with standard methods and tend to maintain the original geometry, even after simplification with a high decimation rate. Our algorithm allows us to interactively render expressive images of complex 3D structures, emphasizing independently plane-like and rod-like structures. The methods are applied for visualization of the microstructure of bone biopsies.

Overview and State-of-the-Art of Uncertainty Visualization

The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.

Membrane Protein Structure, Function and Dynamics: A Perspective from Experiments and Theory

Membrane proteins mediate processes that are fundamental for the flourishing of biological cells. Membrane-embedded transporters move ions and larger solutes across membranes; receptors mediate communication between the cell and its environment and membrane-embedded enzymes catalyze chemical reactions. Understanding these mechanisms of action requires knowledge of how the proteins couple to their fluid, hydrated lipid membrane environment. We present here current studies in computational and experimental membrane protein biophysics, and show how they address outstanding challenges in understanding the complex environmental effects on the structure, function, and dynamics of membrane proteins.

Cactus Grid Computing: Review of Current Development

Cactus is an open source problem solving environment designed for scientists and engineers. Its modular structure facilitates parallel computation across different architectures and collaborative code development between different groups. Here we detail some of the various Grid Tools which have been developed around Cactus, and describe Grid experiments which have been performed to test their application.

The HoneyBee Standard Brain (HSB) – a versatile atlas tool for integrating data and data exchange in the neuroscience community

The HoneyBee Standard Brain (HSB) serves as an interactive tool for comparing morphologies of bee brain neurons and relates it to functional as well as biological properties [1]. Recent efforts by several labs have accumulated confocal image stacks from extraand intracellular stained neurons in the bee central nervous system [2]. We present a pipeline through which confocal images of neurons can be traced and presented in a common space (Figure 1). The first step is an automatic extraction of the neurons skeleton based on threshold segmentation. In a second step this skeleton can be edited using semi-automatic and interactive tools within Amiras Filament Editor. Hereby, the user is assisted by displaying maximum intensity projections and 3D representations in two separate viewers. Next the skeletonized neuron can be labeled (i.e. annotated) by using multiple sets of hierarchically organized label attributes (Figure 2). Finally, the neurons topological and metric features can be visualized, statistically analyzed and/or exported to a simulation package such as Neuron.

Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods

This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The first method is problem-specific and is based on the convex hull of the pelvis. Method two is a more generic approach based on a statistical shape model including the landmarks of interest for every training shape. With our third method we present the most generic approach, where only a small set of training landmarks is required. Those landmarks are transferred to the patient specific geometry based on Mean Value Coordinates (MVCs). The methods work on surfaces of the pelvis that need to be extracted beforehand. We perform this geometry reconstruction with our previously introduced fully automatic segmentation framework for the pelvic bones. With a focus on the accuracy of our novel MVC-based approach, we evaluate and compare our methods on 100 clinical CT datasets, for which gold standard landmarks were defined manually by multiple observers.