Volume visualization in serial electron microscopy using local variance

David Mayerich, John C Hart

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-throughput microscopy allows researchers to produce large volumetric images of biological tissue at sub-micrometer resolution. Serial electron microscopy (EM) has the ability to improve three-dimensional imaging dramatically by providing nanometer-scale resolution. Serial EM data sets of brain tissue can potentially be used to reconstruct the complex structure of biological neural networks. These data sets consist of gigabytes of volumetric data densely packed with anatomical information. This makes three-dimensional EM data sets difficult to visualize. In this paper, we present new methods for visualizing EM data sets using a novel transfer function based on the local variance of volumetric features. We first construct a tensor field that describes the local shape and orientation of structures. These tensors are then used to visualize anatomical features such as cell bodies, membranes, and fibers. We do this by using the tensor shape and orientation to design transfer functions that allow selective visualization of features in dense EM image stacks.

Original languageEnglish (US)
Title of host publicationIEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings
Pages9-16
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event2nd IEEE Symposium on Biological Data Visualization, BioVis 2012 - Seattle, WA, United States
Duration: Oct 14 2012Oct 19 2012

Publication series

NameIEEE Symposium on Biological Data Visualization 2012, BioVis 2012 - Proceedings

Other

Other2nd IEEE Symposium on Biological Data Visualization, BioVis 2012
CountryUnited States
CitySeattle, WA
Period10/14/1210/19/12

Keywords

  • I.4.10 [Image Processing and Computer Vision]: Image Representation - Volumetric
  • I.5.1 [Pattern Recognition]: Models - Statistical

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Bioengineering

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