Multivariate Spatial Data Visualization: A Survey

Multivariate spatial data plays an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research.Comment: 16 pages, 5 figures. Corresponding author: Yubo Ta

A User-friendly Tool for Semi-automated Segmentation and Surface Extraction from Color Volume Data Using Geometric Feature-space Operations

Summary. Segmentation and surface extraction from 3D imaging data is an important task in medical applications. When dealing with scalar data such as CT or MRI scans, a simple thresholding in form of isosurface extraction is an often a good choice. Isosurface extraction is a standard tool for visualizing scalar volume data. Its generalization to color data such as cryosections, however, is not straightforward. In particular, the user interaction in form of selection of the isovalue needs to be replaced by the selection of a three-dimensional region in feature space. We present a user-friendly tool for segmentation and surface extraction from color volume data. Our approach consists of several automated steps and an intuitive mechanism for user-guided feature selection. Instead of overburden the user with complicated operations in feature space, we perform an automated clustering of the occurring colors and suggest segmentations to the users. The suggestions are presented in a color table, from which the user can select the desired cluster. Simple and intuitive refinement methods are provided, in case the automated clustering algorithms did not immediately generate the desired solution exactly. Finally, a marching technique is presented to extract the boundary surface of the desired cluster in object space.