Visual analysis of 3D diffusion tensor fields has become an important topic especially in medical imaging for understanding
microscopic structures and physical properties of biological tissues. However, it is still difficult to continuously track the
underlying features from discrete tensor samples, due to the absence of appropriate interpolation schemes in the sense
that we are able to handle possible degeneracy while fully respecting the smooth transition of tensor anisotropic features.
This is because the degeneracy may cause rotational inconsistency of tensor anisotropy. This paper presents such an
approach to interpolating 3D diffusion tensor fields. The primary idea behind our approach is to resolve the possible
degeneracy through optimizing the rotational transformation between a pair of neighboring tensors by analyzing their
associated eigenstructure, while the degeneracy can be identified by applying a minimum spanning tree-based clustering
algorithm to the original tensor samples. Comparisons with existing interpolation schemes will be provided to demonstrate
the advantages of our scheme, together with several results of tracking white matter fiber bundles in a human brain.
Hierarchical causality relationships reside ubiquitously in the reality. Since the relationships take intricate forms with two kinds of links - hierarchical abstraction and causal association, there exists no single visualization style that allows the user to comprehend them effectively. This paper introduces a novel information visualization framework which can change existing 3D and 2D display styles interactively according to the user's visual analysis demands. The two visualization styles play a complementary role, and the change in the style relies on morphing so as to maintain the user's cognitive map. Based on this framework, we have developed a general-purpose prototype system, which provides the user with an enriched set of functions not only for supporting fundamental information seeking, but bridging analytic gaps to accomplishing high-level analytic tasks such as knowledge discovery and decision making. The effectiveness of the system is illustrated with an application to the analysis of a nuclear-hazard cover-up problem.
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 original quality. These two proposed methods can also be used for batch processing. Several simulated and acquired datasets are used to demonstrate the effects of the methods.
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.
Although texture-based methods provide a very promising way to visualize 3D vector fields, their dense 3D texture hinders the visualization of a 3D volume. In this paper, we introduce the concept of 3D significance map, and describe how significance values are derived from the intrinsic properties of a vector field. Based on the 3D significance map, we can control transfer functions for comprehensible LIC volume rendering by highlighting significant regions and neglecting insignificant information. We also present a 3D streamline illumination model that can reveal the flow direction embedded in a solid LIC texture. Experimental results illustrate the feasibility of our method.