Yuriko Takeshima

Interval volume decomposer: a topological approach to volume traversal

The Interval Volume Decomposer (IVD) is an interface for decomposing an entire volume into interval volumes each of which characterizes a distinctive volume feature. The advantage of the IVD is that it allows us to look inside the volume by peeling interval volumes from outside to inside not only interactively but also automatically. This is achieved due to the rigorous analysis of nested structures of the decomposed interval volumes by constructing a level-set graph that delineates isosurface transitions according to the scalar field. A robust algorithm for computing such level-set graphs is introduced in order to extract significant structures in the volume by putting together local interval volumes into a finite number of global groups. Several decomposition examples of medical and simulated datasets are demonstrated so that the present interface effectively traverses the underlying structures of the volume.

Extraction and LOD control of colored interval volumes

Interval volume serves as a generalized isosurface and represents a three-dimensional subvolume for which the associated scalar filed values lie within a user-specified closed interval. In general, it is not an easy task for novices to specify the scalar field interval corresponding to their ROIs. In order to extract interval volumes from which desirable geometric features can be mined effectively, we propose a suggestive technique which extracts interval volumes automatically based on the global examination of the field contrast structure. Also proposed here is a simplification scheme for decimating resultant triangle patches to realize efficient transmission and rendition of large-scale interval volumes. Color distributions as well as geometric features are taken into account to select best edges to be collapsed. In addition, when a user wants to selectively display and analyze the original dataset, the simplified dataset is restructured to the original quality. Several simulated and acquired datasets are used to demonstrate the effectiveness of the present methods.