Sifting Through Volumes.(research on direct volume rendering at the Vienna University of Technology)(Technology Information)

A new tool mines volume data for the good stuff

The benefits of direct volume rendering in scientific and medical visualization are well known. Because the technique involves mapping all of the volume elements in a given dataset directly into the image plane, it enables the visualization of inner structures of solid objects, as well as the accurate representation of amorphous phenomena, such as clouds, fluids, and gases--none of which can easily be achieved using traditional surface-rendering tools. However, the fact that direct volume rendering takes advantage of all of the volume elements in a dataset is not only its primary advantage, but also its chief disadvantage. The huge amount of data places a significant burden on computational resources, so although "all" of the information for a given volume dataset is theoretically available, the processing requirements limit the practical use of the information.

Because of this shortcoming, researchers are seeking ways to exploit the value of direct volume rendering while minimizing the processing overload. One such promising effort comes out of the Vienna University of Technology, where researchers Jiri Hladuvka and Eduard Groller have devised a technique for automatically identifying objects of significance in a volume dataset. The resulting subset of the original dataset can then be displayed and explored at a fraction of the computational cost.

The automated saliency-identification technique is not the first to define a subset of a volume for display and interaction. A number of methods exist, for example, that identify boundaries of structures within a volume dataset by looking at changes in the intensity of the signal gradient (gradient magnitude). Such information is useful for locating edges in an image, but it is difficult to compute and results in a still-large dataset. Other techniques extract isosurfaces from the original dataset, but this requires that the user specify which aspect of the data he or she wants to look at. In such cases, salient information can easily be overlooked. And still other approaches sample data randomly for reduction, with no eye toward the quality of the data being sampled.

The Vienna researchers have taken a different approach. "The problem addressed in our work is `how' to select the voxels to determine which ones from the volume should be chosen in order to have an interpretable result after visualization," says Hladuvka. The answer they've come up with is a two-stage process in which a filtering technique identifies boundaries and narrow structures within a dataset, and ...