A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification

Xiaobo Zhou; Jun Yang; Meng Wang; Stephen T. Wong

doi:10.1109/LSSA.2006.250403

A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification

Xiaobo Zhou, Jun Yang, Meng Wang, Stephen T. Wong

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

4 Scopus citations

Abstract

Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel Hybrid Merging algorithm for cell nuclei segmentation and propose a novel Favorite Matching phis Local Tree Matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous Hidden Markov Model Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification.

Original language	English (US)
Title of host publication	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs	https://doi.org/10.1109/LSSA.2006.250403
State	Published - Dec 1 2006
Event	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States Duration: Jul 13 2006 → Jul 14 2006

Other

Other	2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
Country/Territory	United States
City	Bethesda, MD
Period	7/13/06 → 7/14/06

ASJC Scopus subject areas

Health(social science)
Assessment and Diagnosis
Medicine(all)
Health Information Management
Electrical and Electronic Engineering
Human-Computer Interaction
Computer Science Applications
Signal Processing

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10.1109/LSSA.2006.250403

Cite this

Zhou, X, Yang, J, Wang, M & Wong, ST 2006, A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification. in 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006., 4015804, 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006, Bethesda, MD, United States, 7/13/06. https://doi.org/10.1109/LSSA.2006.250403

@inproceedings{f0f5a808596c451eb73cbce04164fc76,

title = "A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification",

abstract = "Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel Hybrid Merging algorithm for cell nuclei segmentation and propose a novel Favorite Matching phis Local Tree Matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous Hidden Markov Model Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification.",

author = "Xiaobo Zhou and Jun Yang and Meng Wang and Wong, {Stephen T.}",

year = "2006",

month = dec,

day = "1",

doi = "10.1109/LSSA.2006.250403",

language = "English (US)",

isbn = "1424402786",

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AU - Zhou, Xiaobo

AU - Yang, Jun

AU - Wang, Meng

AU - Wong, Stephen T.

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N2 - Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel Hybrid Merging algorithm for cell nuclei segmentation and propose a novel Favorite Matching phis Local Tree Matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous Hidden Markov Model Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification.

AB - Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel Hybrid Merging algorithm for cell nuclei segmentation and propose a novel Favorite Matching phis Local Tree Matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous Hidden Markov Model Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification.

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BT - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

T2 - 2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006

Y2 - 13 July 2006 through 14 July 2006

ER -

A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification

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