Reconstructing neuronal morphology from microscopy stacks using fast marching

Sreetama Basu, Daniel Racoceanu

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

15 Scopus citations

Abstract

Automated algorithms to build accurate models of 3D neuronal arborization is much in demand due to large volume of microscopy data. We present a tracking algorithm for automatic and reliable extraction of neuronal morphology. It is robust to ambiguous branch discontinuities, variability of intensity and curvature of fibres, arbitrary branch cross-sections, noise and irregular background illumination. We complete the presentation of our method with demonstration of its performance on synthetic data modeling challenging scenarios and on confocal microscopy data of Olfactory Projection fibres from DIADEM data set with promising results.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3597-3601
Number of pages5
ISBN (Electronic)9781479957514
DOIs
StatePublished - 28 Jan 2014
Externally publishedYes

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

  • Fast Forward Marching
  • Gradient Vector Flow
  • Neuronal morphology

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