View-dependent level-of-detail abstraction for interactive atomistic visualization of biological structures. (November 2015)

• Record Type: Journal Article
• Authors: Guo, Dongliang; Nie, Junlan; Liang, Meng; Wang, Yu; Wang, Yanfen; Hu, Zhengping
• Is Part Of: Computers & graphics. Volume 52(2015)
• Page Start: 62
• Page End: 71
• Publication Date: 2015-11
• Subjects: Level of detail -- Clustering -- Space error metric -- Scientific visualization -- Biological structures; Computer graphics -- Periodicals; 006.6
• DOI: 10.1016/j.cag.2015.06.008
• Languages: English
• ISSNs: 0097-8493

Abstract

Abstract: The visualization of biological structures is a challenging task because it requires rendering millions to billions of atoms in real time. In this paper, we propose a view-dependent approach by which a large biological scene can be visualized interactively. In this scheme, we first extract several levels of building blocks of biological structures from a molecular abstraction based on hierarchical clustering. We then define a volume-based distance metric for the clustering process to reduce "inflation" error and propose a quantitative error metric for the object space error evaluation. Finally, we utilize an adaptive screen-space level-of-detail selection with the error metric at run time. Empirical results demonstrate that our molecular hierarchical abstraction method achieves high quality rendering results and performs better than other existing methods. Moreover, our result also shows that the view-dependent approach provides valid results in a large biological scene with more than 10 billion elements. Abstract : Graphical abstract: Abstract : Highlights: A view-dependent macromolecule rendering framework has been designed. Several geometry levels from molecule surfaces of building block were abstracted. A volume-based distance metric was proposed for hierarchical clustering. Approximate object space error metric was proposed for LOD rendering. The view-dependent approach provides valid results in large biological scene with more than 10 billion atoms.