The U.S. Department of Energy's (DOE) Office of Environmental Management (DOE/EM) currently supports an effort to understand and predict the fate of nuclear contaminants and their transport in natural and engineered systems. Geologists, hydrologists, physicists and computer scientists are working together to create models of existing nuclear waste sites, to simulate their behavior and to extrapolate it into the future. We use visualization as an integral part in each step of this process. In the first step, visualization is used to verify model setup and to estimate critical parameters. High-performance computing simulations of contaminant transport produces massive amounts of data, which is then analyzed using visualization software specifically designed for parallel processing of large amounts of structured and unstructured data. Finally, simulation results are validated by comparing simulation results to measured current and historical field data. We describe in this article how visual analysis is used as an integral part of the decision-making process in the planning of ongoing and future treatment options for the contaminated nuclear waste sites. Lessons learned from visually analyzing our large-scale simulation runs will also have an impact on deciding on treatment measures for other contaminated sites.

We present an octree-based approach supporting multiresolution visualization of large three-dimenstional scientific data sets. Given an irregular gridded data set, we initially impose an octree data structure of relatively low resolution, i.e., consisting of a relatively small number of cells. The construction of this initial octree structure is controlled by the original data resolution and cell-specific error value. It is thus possible to use the octree to visualize either the field function value or the local error value. The octreee data structure can be refined further in areas that are specified by a user of a visualization system: A user would identify a region in space, i.e., an octree cell, where the field is of greater interest or where octree cells carry relatively large error values. This usage of our data sturcture ensures that we use the highest resolution to render only regions of interest in a large-scale scientific data set.

Large biomedical volumetric data sets are usually stored as file sets, where the files represent a family of cross sections. Interactive rendering of large data sets requires fast access to user-defined parts of the data, because it is virtually impossible to render an entire data set of such an enormous size (several gigabytes) at full resolution, and to transfer such data upon request over the Internet in a reasonable amount of time. Therefore, hierarchical rendering techniques have been introduced to render a region of interest at a relatively higher resolution. Regions rendered at coarser resolutions are provided as context information. We present a dynamic subdivision scheme that incorporates space-subdivision and wavelet compression.

Hierarchical, texture-based rendering is a key technology for exploring largescale data sets. We describe a framework for an interactive rendering system based on a client/server model. The system supports various output media from immersive 3-D environments to desktop based rendering systems. It uses web-based transport mechanisms to transfer the data between the server and the client application. This allows us to access and explore large-scale data sets from remote locations over the Internet. Hierarchical space-subdivision,wavelet compression, and progressive data transmission are used to visualize the data on the client side.

We present a semi-automatic technique for segmenting a large cryo-sliced human brain data set that contains 753 highresolution RGB color images. This human brain data set presents a number of unique challenges to segmentation and visualization due to its size (over 7 GB) as well as the fact that each image not only shows the current slice of the brain but also unsliced ''deeper layers'' of the brain. These challenges are not present in traditional MRI and CT data sets. We have found that segmenting this data set can be made easier by using the YIQ color model and morphology. We have used a hardware-assisted interactive volume renderer to evaluate our segmentation results.