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Jeong, Won-Ki
High-performance Visual Computing Lab
Research Interests
  • Visualization, image processing, machine learning, parallel computing

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Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets

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Title
Ssecrett and NeuroTrace: Interactive Visualization and Analysis Tools for Large-Scale Neuroscience Data Sets
Author
Jeong, Won-KiBeyer, JohannaHadwiger, MarkusBlue, RustyLaw, CharlesVazquez-Reina, AmelioReid, R. ClayLichtman, JeffPfister, Hanspeter
Keywords
Computer graphics; Connectome; Graphics and multimedia; Graphics hardware; Implicit surface rendering; Neuroscience; Segmentation; Volume rendering
Issue Date
2010-05
Publisher
IEEE COMPUTER SOC
Citation
IEEE COMPUTER GRAPHICS AND APPLICATIONS, v.30, no.3, pp.58 - 70
Abstract
Recent advances in optical and electron microscopy let scientists acquire extremely high-resolution images for neuroscience research. Data sets imaged with modern electron microscopes can range between tens of terabytes to about one petabyte. These large data sizes and the high complexity of the underlying neural structures make it very challenging to handle the data at reasonably interactive rates. To provide neuroscientists flexible, interactive tools, the authors introduce Ssecrett and NeuroTrace, two tools they designed for interactive exploration and analysis of large-scale optical- and electron-microscopy images to reconstruct complex neural circuits of the mammalian nervous system.
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DOI
10.1109/MCG.2010.56
ISSN
0272-1716
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EE_Journal Papers
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