Proceedings: GI 2010

Interactive illustrative visualization of hierarchical volume data

Jean-Paul Balabanian, Ivan Viola, Eduard Gröller

Proceedings of Graphics Interface 2010: Ottawa, Ontario, Canada, 31 May - 2 June 2010, 137-144

  • BibTex

    author = {Balabanian, Jean-Paul and Viola, Ivan and Gr{\"o}ller, Eduard},
    title = {Interactive illustrative visualization of hierarchical volume data},
    booktitle = {Proceedings of Graphics Interface 2010},
    series = {GI 2010},
    year = {2010},
    issn = {0713-5424},
    isbn = {978-1-56881-712-5},
    location = {Ottawa, Ontario, Canada},
    pages = {137--144},
    numpages = {8},
    publisher = {Canadian Human-Computer Communications Society},
    address = {Toronto, Ontario, Canada},


In scientific visualization the underlying data often has an inherent abstract and hierarchical structure. Therefore, the same dataset can simultaneously be studied with respect to its characteristics in the three-dimensional space and in the hierarchy space. Often both characteristics are equally important to convey. For such scenarios we explore the combination of hierarchy visualization and scientific visualization, where both data spaces are effectively integrated. We have been inspired by illustrations of species evolutions where hierarchical information is often present. Motivated by these traditional illustrations, we introduce integrated visualizations for hierarchically organized volumetric datasets. The hierarchy data is displayed as a graph, whose nodes are visually augmented to depict the corresponding 3D information. These augmentations include images due to volume raycasting, slicing of 3D structures, and indicators of structure visibility from occlusion testing. New interaction metaphors are presented that extend visualizations and interactions, typical for one visualization space, to control visualization parameters of the other space. Interaction on a node in the hierarchy influences visual representations of 3D structures and vice versa. We integrate both the abstract and the scientific visualizations into one view which avoids frequent refocusing typical for interaction with linked-view layouts. We demonstrate our approach on different volumetric datasets enhanced with hierarchical information.