Abstract In this paper we discuss three scientific computing problem solving environments: SCIRun, BioPSE, and Uintah. We begin with an overview of the systems, describe their underlying software architectures, discuss implementation issues, and give examples of their use in computational science and engineering applications. We conclude by discussing future research and development plans for the three problem solving environments. Copyright © 2002 John Wiley & Sons, Ltd.

A standard way to segment medical imaging datasets is by tracing contours around regions of interest in parallel planar slices. Unfortunately, the standard methods for reconstructing three dimensional surfaces from those planar contours tend to be either complicated or not very robust. Furthermore, they fail to consistently mesh abutting structures which share portions of contours. We present a novel, straight-forward algorithm for accurately and automatically reconstructing surfaces from planar contours. Our algorithm is based on scanline rendering and separating surface extraction. By rendering the contours as distinctly colored polygons and reading back each rendered slice into a segmented volume, we reduce the complex problem of building a surface from planar contours to the much simpler problem of extracting separating surfaces from a classified volume. Our scanline surfacing algorithm robustly handles complex surface topologies such as bifurcations, embedded features and abutting surfaces.

Diffusion-weighted magnetic resonance imaging is a relatively new modality capable of elucidating the fibrous structure of certain types of tissue, such as the white matter within the brain. One tool for interpreting this data is volume rendering because it permits the visualization of three dimensional structure without a prior segmentation process. In order to use volume rendering, however, we must develop methods for assigning opacity and color to the data, and create a method to shade the data to improve the legibility of the rendering. Previous work introduced three such methods: barycentric opacity maps, hue-balls (for color), and lit-tensors (for shading). The paper expands on and generalizes these methods, describing and demonstrating further means of generating opacity, color, and shading from the tensor information. We also propose anisotropic reaction-diffusion volume textures as an additional tool for visualizing the structure of diffusion data. The patterns generated by this process can be visualized on their own or they can be used to supplement the volume rendering strategies described in the rest of the paper. Finally, because interpolation between data points is a fundamental issue in volume rendering, we conclude with a discussion and evaluation of three distinct interpolation methods suitable for diffusion tensor MRI data.

With the development of magnetic resonance imaging techniques for acquiring diffusion tensor data from biological tissue, visualization of tensor data has become a new research focus. The diffusion tensor describes the directional dependence of water molecules' diffusion and can be represented by a three-by-three symmetric matrix. Visualization of second-order tensor fields is difficult because the data values have many degrees of freedom. Existing visualization techniques are best at portraying the tensor's properties over a two-dimensional field, or over a small subset of locations within a three-dimensional field. A means of visualizing the global structure in measured diffusion tensor data is needed. We propose the use of direct volume rendering, with novel approaches for the tensors' coloring, lighting, and opacity assignment. Hue-balls use a two-dimensional colormap on the unit sphere to illustrate the tensor's action as a linear operator. Lit-tensors provide a lighting model for tensors which includes as special cases both lit-lines (from streamline vector visualization) and standard Phong surface lighting. Together with an opacity assignment based on a novel two-dimensional barycentric space of anisotropy, these methods are shown to produce informative renderings of measured diffusion tensor data from the human brain.

Tracking linear features through tensor field datasets is an open research problem with widespread utility in medical and engineering disciplines. Existing tracking methods, which consider only the preferred local diffusion direction as they propagate, fail to accurately follow features as they enter regions of local complexity. This shortcoming is a result of partial voluming; that is, voxels in these regions often contain contributions from multiple features. These combined contributions result in ambiguities when deciding local primary feature orientation based solely on the preferred diffusion direction. We introduce a novel feature extraction method which we term tensorline propagation. Our method resolves the above ambiguity by incorporating information about the nearby orientation of the feature, as well as the anisotropic classification of the local tensor. The nearby orientation information is added in the spirit of an advection term in a standard diffusion based propagation technique, and has the effect of stabilizing the tracking. To demonstrate the efficacy of tensorlines, we apply this method to the neuroscience problem of tracking white-matter bundles within the brain.

Traditionally, texture coordinates have been generated based solely on the model’s geometry, often even before a model’s textures have been created. With the arrival of new technologies, such as 3D paint programs, weaknesses of a static optimization pre‐process are becoming apparent. These weaknesses arise from constructing a parameterization based solely on the model’s geometry, ignoring the fact that detail is not uniformly spaced throughout the texture space. In fact, certain regions of the texture are more important than other regions. In this paper we introduce the notion of the "importance map" and describe how importance values are derived from both intrinsic properties of the texture and user‐guided highlights. Furthermore, we describe how importance maps are used to drive the texture coordinate optimization. Finally, we show how this optimization process can be integrated into a 3D painting environment, enabling periodic optimization at any stage of texture design.