Image analysis of dynamic brain activity based on gray distance compensation

Ying Wang, Yung‐Tian A. Gau, Hanh N.D. Le, Dwight E. Bergles, Jin U. Kang

Research output: Contribution to journalArticlepeer-review

Abstract

Assessing time‐dependent changes in brain activity is of crucial importance in neuroscience. Here, we propose a novel image processing method to automatically identify active regions and assess time‐dependent changes in fluorescence arising from genetically encoded indicators of activity. First, potential active regions and the corresponding active centers were extracted based on gray distance compensation. Then potential active regions were aligned through frames and, if meeting pre‐determined intensity criteria, were accepted as active regions and the fluorescence changes were quantified. We validated this method with independent in vivo imaging datasets collected from transgenic mice that express the genetically encoded calcium indicator GCaMP3. Our studies indicate that the incorporation of this gray distance compensation‐based algorithm substantially improves the accuracy and efficiency of detecting and quantifying cellular activity in the intact brain.

Original languageEnglish (US)
Article number858
JournalApplied Sciences (Switzerland)
Volume7
Issue number8
DOIs
StatePublished - Aug 19 2017

Keywords

  • Automated cell tracing
  • Functional brain imaging
  • Gray distance transformation

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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