Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.
|Original language||English (US)|
|Title of host publication||2012 IEEE Symposium on Biological Data Visualization (BioVis)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Oct 2012|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This publication is based on work supported by Award No.KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST), DOE SciDAC:VACET, NSFOCI-0906379, NIH-1R01GM098151-01. We also wish to thankthe reviewers for their suggestions, and thank Chems Touatiof SCI for making the demo video.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.