Volume data arises in a number of important applications in visualization, including CT, MRI, X-ray crystallography, and computational fluid dynamics. We survey the two methods, volume visualization and isosurface extraction, that have been applied to understanding and imaging these data sets.

We present a preprocessing method for organizing discrete scalar volume data of arbitrary dimension on external storage with important applications to out-of-core volume visualization of extremely large data sets. The potential applications include extracting isosurfaces in a manner that minimizes both I/O and disk seek time, a priori topologically correct isosurface simplification (prior to extraction), managing level of detail of rendering in an intelligent manner and producing a visual atlas
of all topologically distinct isosurface bounded objects in the data set, with the range of scalar isovalues that reveal each. Unlike the related work on contour trees, our techniques work for both irregularly and regularly girded data, and no perturbation of the data is required.