BROWSE

Related Researcher

Author's Photo

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

ITEM VIEW & DOWNLOAD

Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets

Cited 20 times inthomson ciCited 27 times inthomson ci
Title
Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets
Author
Jeong, Won-KiBeyer, JohannaHadwiger, MarkusVazquez, AmelioPfister, HanspeterWhitaker, Ross T.
Keywords
connectome; graphics hardware; implicit surface rendering; neuroscience; Segmentation; volume rendering
Issue Date
2009-11
Publisher
IEEE COMPUTER SOC
Citation
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.15, no.6, pp.1505 - 1514
Abstract
Recent advances in scanning technology provide high resolution EM (Electron Microscopy) datasets that allow neuroscientists to reconstruct complex neural connections in a nervous system. However, due to the enormous size and complexity of the resulting data, segmentation and visualization of neural processes in EM data is usually a difficult and very time-consuming task. In this paper, we present NeuroTrace, a novel EM volume segmentation and visualization system that consists of two parts: a semi-automatic multiphase level set segmentation with 3D tracking for reconstruction of neural processes, and a specialized volume rendering approach for visualization of EM volumes. It employs view-dependent on-demand filtering and evaluation of a local histogram edge metric, as well as on-the-fly interpolation and ray-casting of implicit surfaces for segmented neural structures. Both methods are implemented on the GPU for interactive performance. NeuroTrace is designed to be scalable to large datasets and data-parallel hardware architectures. A comparison of NeuroTrace with a commonly used manual EM segmentation tool shows that our interactive workflow is faster and easier to use for the reconstruction of complex neural processes.
URI
Go to Link
DOI
10.1109/TVCG.2009.178
ISSN
1077-2626
Appears in Collections:
EE_Journal Papers
Files in This Item:
2-s2.0-70350630741.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU