Automatic tracking of proteins in sequences of fluorescence images

Xia Li; Mingming Hao; David W. Piston; Benoit M. Dawanti

doi:10.1117/12.533726

Automatic tracking of proteins in sequences of fluorescence images

Xia Li, Mingming Hao, David W. Piston, Benoit M. Dawanti

Research output: Contribution to journal › Conference article › peer-review

5 Scopus citations

Abstract

The development of digital microscopy and computational power is providing new opportunities for analyzing the motility of the vesicles (proteins) within living cells. In this paper, an automatic method is developed to segment and track vesicles in large amount of fluorescence images, in order to compute a number of quantitative parameters such as displacement, residence time, binding, or immobile fraction. We present a method that permits the automatic tracking of subcellular structures in long sequences of fluorescence images (up to 100 frames). The method we propose has been tested on 92 data sets totaling 8225 frames.

Original language	English
Pages (from-to)	1364-1371
Number of pages	8
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5370 II
DOIs	https://doi.org/10.1117/12.533726
State	Published - 2004
Event	Progress in Biomedical Optics and Imaging - Medical Imaging 2004: Imaging Processing - San Diego, CA, United States Duration: Feb 16 2004 → Feb 19 2004

Keywords

Image segmentation
Motion model
Tracking algorithm
Weighted similarity measurement

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10.1117/12.533726

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title = "Automatic tracking of proteins in sequences of fluorescence images",

abstract = "The development of digital microscopy and computational power is providing new opportunities for analyzing the motility of the vesicles (proteins) within living cells. In this paper, an automatic method is developed to segment and track vesicles in large amount of fluorescence images, in order to compute a number of quantitative parameters such as displacement, residence time, binding, or immobile fraction. We present a method that permits the automatic tracking of subcellular structures in long sequences of fluorescence images (up to 100 frames). The method we propose has been tested on 92 data sets totaling 8225 frames.",

keywords = "Image segmentation, Motion model, Tracking algorithm, Weighted similarity measurement",

author = "Xia Li and Mingming Hao and Piston, {David W.} and Dawanti, {Benoit M.}",

year = "2004",

doi = "10.1117/12.533726",

language = "English",

volume = "5370 II",

pages = "1364--1371",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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TY - JOUR

T1 - Automatic tracking of proteins in sequences of fluorescence images

AU - Li, Xia

AU - Hao, Mingming

AU - Piston, David W.

AU - Dawanti, Benoit M.

PY - 2004

Y1 - 2004

N2 - The development of digital microscopy and computational power is providing new opportunities for analyzing the motility of the vesicles (proteins) within living cells. In this paper, an automatic method is developed to segment and track vesicles in large amount of fluorescence images, in order to compute a number of quantitative parameters such as displacement, residence time, binding, or immobile fraction. We present a method that permits the automatic tracking of subcellular structures in long sequences of fluorescence images (up to 100 frames). The method we propose has been tested on 92 data sets totaling 8225 frames.

AB - The development of digital microscopy and computational power is providing new opportunities for analyzing the motility of the vesicles (proteins) within living cells. In this paper, an automatic method is developed to segment and track vesicles in large amount of fluorescence images, in order to compute a number of quantitative parameters such as displacement, residence time, binding, or immobile fraction. We present a method that permits the automatic tracking of subcellular structures in long sequences of fluorescence images (up to 100 frames). The method we propose has been tested on 92 data sets totaling 8225 frames.

KW - Image segmentation

KW - Motion model

KW - Tracking algorithm

KW - Weighted similarity measurement

UR - http://www.scopus.com/inward/record.url?scp=5644302308&partnerID=8YFLogxK

U2 - 10.1117/12.533726

DO - 10.1117/12.533726

M3 - Conference article

AN - SCOPUS:5644302308

SN - 0277-786X

VL - 5370 II

SP - 1364

EP - 1371

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Progress in Biomedical Optics and Imaging - Medical Imaging 2004: Imaging Processing

Y2 - 16 February 2004 through 19 February 2004

ER -

Automatic tracking of proteins in sequences of fluorescence images

Abstract

Keywords

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