A new algorithm for 3D dendritic spine detection

Wengang Zhou, Houqiang Li, Xiaobo Zhou, Stephen Wong

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

2 Scopus citations

Abstract

It has been shown in recent research that there is a close relationship between neurological functions of neuron and its morphology. As manual analysis of large data sets is too tedious and may be subjected to user bias, a computer aided processing method is urgently desired. In this paper, we propose an automatic approach for 3D dendritic spine detection, which can greatly help neuron-biologists to obtain morphological information about a neuron and its spines. The work mainly consists of segmentation and spine component detection. The segmentation of dendrite and spine components is carried out by means of 3D level set based on local binary fitting model, which yields better results than global threshold method. As for spine component detection, an efficient approach is presented which consists of backbone extraction, detached and attached spine components detection. The detection is robust to noise and the detected spines are well represented. We validate our algorithm with real 3D neuron images and the result reveals that it works well.

Original languageEnglish (US)
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages137-146
Number of pages10
Volume952
DOIs
StatePublished - Dec 1 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: Dec 17 2007Dec 19 2007

Other

Other2007 International Symposium on Computational Models for Life Sciences, CMLS '07
CountryAustralia
CityGold Coast, QLD
Period12/17/0712/19/07

Keywords

  • 3D level set
  • Grassfire
  • LBF model
  • Segmentation
  • Spine detection

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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