A Bayesian method for 3D estimation of subcellular particle features in multi-angle TIRF microscopy

Liang Liang, Hongying Shen, Yingke Xu, Pietro De Camilli, Derek K. Toomre, James S. Duncan

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

8 Scopus citations

Abstract

Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a relatively new and powerful tool to study subcellular particles near cell membrane due to its unique illumination mechanism. We present a MAP-Bayesian method to automatically estimate features of individual particles in MA-TIRF images, including 3D positions, relative sizes, and relative amount of fluorophores. Using the MAP criterion, the optimal values of the features can be obtained by maximizing a nonlinear functional. Initial feature values are estimated by using image filters and clustering algorithms. The method is evaluated on synthetic data and results show that it has high accuracy. The result on real data from our initial experiments is also presented.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages984-987
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
Country/TerritorySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • Bayesian estimation
  • subcellular particle detection
  • total internal reflection fluorescence microscopy

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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