An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

Jun Yan; Xiaobo Zhou; Qiong Yang; Ning Liu; Qiansheng Cheng; Stephen T C Wong

doi:10.1109/ICIP.2006.313143

An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

Jun Yan, Xiaobo Zhou, Qiong Yang, Ning Liu, Qiansheng Cheng, Stephen T C Wong

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

9 Scopus citations

Abstract

The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

Original language	English (US)
Title of host publication	2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages	1917-1920
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2006.313143
State	Published - 2006
Event	2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States Duration: Oct 8 2006 → Oct 11 2006

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Other

Other	2006 IEEE International Conference on Image Processing, ICIP 2006
Country/Territory	United States
City	Atlanta, GA
Period	10/8/06 → 10/11/06

Keywords

Biological systems
Biomedical image processing
Biomedical signal processing

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2006.313143

Cite this

Yan, J., Zhou, X., Yang, Q., Liu, N., Cheng, Q., & Wong, S. T. C. (2006). An effective system for optical microscopy cell image segmentation, tracking and cell phase identification. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 1917-1920). Article 4106930 (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.313143

An effective system for optical microscopy cell image segmentation, tracking and cell phase identification. / Yan, Jun; Zhou, Xiaobo; Yang, Qiong et al.
2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings. 2006. p. 1917-1920 4106930 (Proceedings - International Conference on Image Processing, ICIP).

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

Yan, J, Zhou, X, Yang, Q, Liu, N, Cheng, Q & Wong, STC 2006, An effective system for optical microscopy cell image segmentation, tracking and cell phase identification. in 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings., 4106930, Proceedings - International Conference on Image Processing, ICIP, pp. 1917-1920, 2006 IEEE International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 10/8/06. https://doi.org/10.1109/ICIP.2006.313143

@inproceedings{ece7439a9fef48adb002cf1977c8cfee,

title = "An effective system for optical microscopy cell image segmentation, tracking and cell phase identification",

abstract = "The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.",

keywords = "Biological systems, Biomedical image processing, Biomedical signal processing",

author = "Jun Yan and Xiaobo Zhou and Qiong Yang and Ning Liu and Qiansheng Cheng and Wong, {Stephen T C}",

year = "2006",

doi = "10.1109/ICIP.2006.313143",

language = "English (US)",

isbn = "1424404819",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "1917--1920",

booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",

}

TY - GEN

T1 - An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

AU - Yan, Jun

AU - Zhou, Xiaobo

AU - Yang, Qiong

AU - Liu, Ning

AU - Cheng, Qiansheng

AU - Wong, Stephen T C

PY - 2006

Y1 - 2006

N2 - The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

AB - The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

KW - Biological systems

KW - Biomedical image processing

KW - Biomedical signal processing

UR - http://www.scopus.com/inward/record.url?scp=73849107097&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=73849107097&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2006.313143

DO - 10.1109/ICIP.2006.313143

M3 - Conference contribution

AN - SCOPUS:73849107097

SN - 1424404819

SN - 9781424404810

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1917

EP - 1920

BT - 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings

T2 - 2006 IEEE International Conference on Image Processing, ICIP 2006

Y2 - 8 October 2006 through 11 October 2006

ER -

An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this