Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis

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

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

4 Scopus citations

Abstract

Recent advancement in live cell fluorescence microscopy has enabled image acquisition at single particle resolution, through which biologists can investigate the underlying mechanisms of cellular processes. In this paper, we present a method to automatically detect the features of sub-cellular particles in 2D fluorescence images, including x-y positions, fluorescence intensities, and relative sizes. The method consists of two parts. One is an initial detection method, which finds particle candidates in the images using image filters and clustering algorithms. The other is a MAP-Bayesian based estimation method, which provides the optimal estimations of particle features. The method is evaluated on synthetic data and results show that it has high accuracy. The results on real data confirmed by human expert cell biologists are also presented.

Original languageEnglish (US)
Title of host publication2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012
Pages161-166
Number of pages6
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012 - Breckenridge, CO, United States
Duration: Jan 9 2012Jan 10 2012

Publication series

NameProceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

Conference

Conference2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012
Country/TerritoryUnited States
CityBreckenridge, CO
Period1/9/121/10/12

ASJC Scopus subject areas

  • Applied Mathematics
  • Radiology Nuclear Medicine and imaging
  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis'. Together they form a unique fingerprint.

Cite this