›› 2011, Vol. 26 ›› Issue (2): 217-228.doi: 10.1007/s11390-011-1124-9

• Artificial Intelligence • Previous Articles     Next Articles

Visibility-Aware Direct Volume Rendering

Wai-Ho Mak1 (麦伟豪), Yingcai Wu (巫英才)2, Ming-Yuen Chan1 (陈明远), and Huamin Qu1 (屈华民), Member, IEEE   

  1. 1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology Hong Kong, China;
    2. Department of Computer Science, The University of California, Davis, CA, U.S.A.
  • Received:2009-07-24 Revised:2010-11-28 Online:2011-03-05 Published:2011-03-05
  • About author:Wai-Ho Mak received the B.Eng. degree (first-class honors) in computer science and the M.Phil. degree in computer science and engineering both from the Hong Kong University of Science and Technology (HKUST) in 2007 and 2009 respectively. His research interests include scientific visualization and information visualization.
    Yingcai Wu is a post-doctoral researcher in the Department of Computer Science at The University of California, Davis. He obtained his B.Eng. degree (2004) in computer science and technology from South China University of Technology, China, and his Ph.D. (2009) degree in computer science and engineering from the HKUST. His research interests are in medical volume visualization and visual analytics.
    Ming-Yuen Chan received the B.Eng. degree (2003) in computer engineering from the University of Hong Kong and the M.Phil. (2006) and the Ph.D. degrees (2009) in computer science from the HKUST. His research interests include scientific visualization and medical imaging.
    Huamin Qu is an associate professor in the Department of Computer Science and Engineering at the HKUST. His main research interests are in visualization and computer graphics. He has conducted a wide range of research on scientific visualization, information visualization, real time graphics systems, virtual reality, and medical imaging. He received the B.S. degree in mathematics from Xi'an Jiaotong University, China, the M.S. and the Ph.D. degrees in computer science from the Stony Brook University, New York.
  • Supported by:

    The work is supported in part by Hong Kong RGC CERG under Grant No. 618705.

Direct volume rendering (DVR) is a powerful visualization technique which allows users to effectively explore and study volumetric datasets. Different transparency settings can be flexibly assigned to different structures such that some valuable information can be revealed in direct volume rendered images (DVRIs). However, end-users often feel that some risks are always associated with DVR because they do not know whether any important information is missing from the transparent regions of DVRIs. In this paper, we investigate how to semi-automatically generate a set of DVRIs and also an animation which can reveal information missed in the original DVRIs and meanwhile satisfy some image quality criteria such as coherence. A complete framework is developed to tackle various problems related to the generation and quality evaluation of visibility-aware DVRIs and animations. Our technique can reduce the risk of using direct volume rendering and thus boost the confidence of users in volume rendering systems.

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