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 Citations (Scopus)

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

Fingerprint

Data reduction
Visualization
Transfer functions
Medical applications
Display devices

All Science Journal Classification (ASJC) codes

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

Cite this

@article{d4bad554f92d45338d5c1b837a88a9a2,
title = "Feature-preserving volume data reduction and focus+context visualization",
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.",
author = "Wang, {Yu Shuen} and Chaoli Wang and Tong-Yee Lee and Ma, {Kwan Liu}",
year = "2011",
month = "1",
day = "1",
doi = "10.1109/TVCG.2010.34",
language = "English",
volume = "17",
pages = "171--181",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "2",

}

Feature-preserving volume data reduction and focus+context visualization. / Wang, Yu Shuen; Wang, Chaoli; Lee, Tong-Yee; Ma, Kwan Liu.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 17, No. 2, 5416703, 01.01.2011, p. 171-181.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Feature-preserving volume data reduction and focus+context visualization

AU - Wang, Yu Shuen

AU - Wang, Chaoli

AU - Lee, Tong-Yee

AU - Ma, Kwan Liu

PY - 2011/1/1

Y1 - 2011/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78650168775&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78650168775&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2010.34

DO - 10.1109/TVCG.2010.34

M3 - Article

C2 - 21149874

AN - SCOPUS:78650168775

VL - 17

SP - 171

EP - 181

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 2

M1 - 5416703

ER -