Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
Yarden Livnat, Xavier Tricoche

We have proposed a novel point-based approach to view dependent isosurface extraction. We also introduce a fast visibility query system for the view dependent traversal, which exhibits moderate memory requirements. Using this technique, we achieved an interactive interrogation of the full visible woman dataset (1GB) at more then four frames per second on a desktop computer. The point-based approach is based on an extraction scheme that classifies different sections of the isosurface into four categories. The classification is based on the size of the geometry when projected onto the screen. In particular, we use points to represent small and sub-pixel triangles, as well as large sections of the isosurface whose projection has sub-pixel size. An important issue raised by point-based processing is how to assign a normal to an isolated point representing a large, but far, section of the isosurface. We propose to define such normals during a post processing of the extracted isosurface and provide the corresponding hardware implementation.

isosurface fig1 Figure 1: Left: A section of the visible female skeleton. Middle: A closeup view of the extracted points. Right: The final visibility mask. The color represent different levels of the mask hierarchy


Isosurface extraction is an important technique for visualizing three-dimensional scalar fields. By exposing contours of constant value, isosurfaces provide a mechanism for understanding the structure of scalar data. These contours isolate surfaces of interest, focusing the analysis on important features in the data, such as material boundaries, while suppressing extraneous information.

Isosurface extraction poses a unique challenge in that no geometry exists before the user provides an isovalue. Furthermore, the user may change the isovalue often, and any geometry extracted based on the previous isovalue should be discarded. Consequently, only a limited amount of meta data can be generated and stored for use during an interactive session. In the case of large datasets this problem is worse because the size of the scalar information leaves little memory for additional data structures. In fact, the size of very large datasets can overwhelm the extraction and the rendering systems. For example, the isosurface corresponding to the skeleton in the Visible Woman dataset (1GB) contains over eleven million triangles, which clearly over strains what today's graphics cards can handle interactively.

To address this issue, we have proposed an output sensitive approach based on view-dependent extraction of the isosurface. Others have since proposed various acceleration techniques using massive parallel machines or the graphics hardware. Yet, none of these approaches address the need for an interactive extraction of large datasets using a single desktop computer. This research presents a point-based view-dependent isosurface extraction technique that permits the isosurfaces of data set, fitting in the memory of a desktop computer, to extract and render at interactive frame rates.

Y. Livnat, X. Tricoche. “Interactive Point Based Isosurface Extraction,” In Proceeding of IEEE Visualization 2004, pp. 457--464. 2004.
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