The Visual Computer

, Volume 31, Issue 4, pp 441–454 | Cite as

Model-driven multicomponent volume exploration

  • Enya ShenEmail author
  • Jiazhi Xia
  • Zhiquan Cheng
  • Ralph R. Martin
  • Yunhai Wang
  • Sikun Li
Original Article


The current multicomponent volume segmentation and labeling methods are mostly hard to get correct segmentation and labeling results automatically and rely hardly on experts’ aids, which make related volume exploration to be time consuming, laborious and prone to errors and omissions. To solve this problem, we present a novel volume exploration method driven by admitted model. We first apply Gaussian mixture models to segment the raw volume. However, different components with similar value are still mixed. To segment these components further, we make use of region-grown principle to produce a fine-grained part segmentation. To label different parts automatically, we found that it is helpful to take advantage of annotated model, like human anatomy model (PlasticboyCC,, 2013). However, it is not straightforward to label segmented volume with geometric model automatically. Inspired by electors voting (Au et al., Comput Graph Forum 29:645–654, 2010), we propose a volume-model correspondence schema to overcome this intractable challenge. Moreover, it is essential to exploit intuitive interactive methods for interactive exploration, so we also developed practical precise interaction techniques to assist volume exploration. Our experiments with various data and discussion with specialists show that our method provides an efficient and impactful way to explore volume data.


Interactive volume visualization  Volume segmentation  Volume correspondence Knowledge-assisted visualization 



The authors would like to thank anonymous reviewers at TVCJ for their comments that helped us to improve the quality of this manuscript. The authors would also like to thank J.Y. Huang for checking reading of this manuscript. This research is supported by the National Natural Science Foundation of China under Grant No. 61170157, and the National Grand Fundamental Research 973 Program of China under Grant No. G2009CB72380.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Enya Shen
    • 1
    Email author
  • Jiazhi Xia
    • 2
  • Zhiquan Cheng
    • 1
  • Ralph R. Martin
    • 3
  • Yunhai Wang
    • 4
  • Sikun Li
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.School of Information Science and EngineeringCentral South UniversityChangshaChina
  3. 3.School of Computer Science and InformaticsCardiff UniversityCardiffWales, UK
  4. 4.Shenzhen VisuCA Key Lab/SIATShenzhenChina

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