Automatic analysis and quantification of fluorescently-labeled synapses in microscope images

Shai Yona, Alex Katsman, Ayelet Orenbuch, Daniel Gitler, Yitzhak Yitzhaky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The purpose of this work is to classify and quantify synapses and their properties in the cultures of a mouse's hippocampus, from images acquired by a fluorescent microscope. Quantification features include the number of synapses, their intensity and their size characteristics. The images obtained by the microscope contain hundreds to several thousands of synapses with various elliptic-like shape features and intensities. These images also include other features such as glia cells and other biological objects beyond the focus plane; those features reduce the visibility of the synapses and interrupt the segmentation process. The proposed method comprises several steps, including background subtraction, identification of suspected centers of synapses as local maxima of small neighborhoods, evaluation of the tendency of objects to be synapses according to intensity properties at their larger neighborhoods, classification of detected synapses into categories as bulks or single synapses and finally, delimiting the borders of each synapse.

Original languageAmerican English
Title of host publicationApplications of Digital Image Processing XXXIV
DOIs
StatePublished - 31 Oct 2011
EventApplications of Digital Image Processing XXXIV - San Diego, CA, United States
Duration: 22 Aug 201124 Aug 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8185

Conference

ConferenceApplications of Digital Image Processing XXXIV
Country/TerritoryUnited States
CitySan Diego, CA
Period22/08/1124/08/11

Keywords

  • Synapse detection
  • microscopic image analysis
  • synapse segmentation

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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