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

doi:10.1109/MMBIA.2012.6164750

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 proceeding › Conference 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 language	English (US)
Title of host publication	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012
Pages	161-166
Number of pages	6
DOIs	https://doi.org/10.1109/MMBIA.2012.6164750
State	Published - 2012
Externally published	Yes
Event	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012 - Breckenridge, CO, United States Duration: Jan 9 2012 → Jan 10 2012

Publication series

Name	Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

Conference

Conference	2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012
Country/Territory	United States
City	Breckenridge, CO
Period	1/9/12 → 1/10/12

ASJC Scopus subject areas

Applied Mathematics
Radiology Nuclear Medicine and imaging
Biomedical Engineering

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10.1109/MMBIA.2012.6164750

Cite this

Liang, L., Xu, Y., Shen, H., De Camilli, P., Toomre, D. K., & Duncan, J. S. (2012). Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012 (pp. 161-166). [6164750] (Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis). https://doi.org/10.1109/MMBIA.2012.6164750

Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. / Liang, Liang; Xu, Yingke; Shen, Hongying; De Camilli, Pietro; Toomre, Derek K.; Duncan, James S.

2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012. 2012. p. 161-166 6164750 (Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liang, L, Xu, Y, Shen, H, De Camilli, P, Toomre, DK & Duncan, JS 2012, Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. in 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012., 6164750, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 161-166, 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012, Breckenridge, CO, United States, 1/9/12. https://doi.org/10.1109/MMBIA.2012.6164750

Liang L, Xu Y, Shen H, De Camilli P, Toomre DK, Duncan JS. Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. In 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012. 2012. p. 161-166. 6164750. (Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis). https://doi.org/10.1109/MMBIA.2012.6164750

Liang, Liang ; Xu, Yingke ; Shen, Hongying ; De Camilli, Pietro ; Toomre, Derek K. ; Duncan, James S. / Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis. 2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA 2012. 2012. pp. 161-166 (Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis).

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title = "Automatic detection of subcellular particles in fluorescence microscopy via feature clustering and bayesian analysis",

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.",

author = "Liang Liang and Yingke Xu and Hongying Shen and {De Camilli}, Pietro and Toomre, {Derek K.} and Duncan, {James S.}",

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AU - Duncan, James S.

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AB - 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.

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

Abstract

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