Feature-preserving volume data reduction and focus+context visualization

Yu Shuen Wang, Chaoli Wang, Tong Yee Lee, Kwan-Liu Ma

Research output: Contribution to journalArticle

42 Scopus citations

Abstract

The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.

Original languageEnglish (US)
Article number5416703
Pages (from-to)171-181
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume17
Issue number2
DOIs
StatePublished - Jan 1 2011

Keywords

  • Data reduction
  • focus+context visualization
  • interactive visualization
  • mesh deformation
  • transfer functions
  • volume rendering.

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Feature-preserving volume data reduction and focus+context visualization'. Together they form a unique fingerprint.

  • Cite this