Cell Phase Identification Using Fuzzy Gaussian Mixture Models

Dat Tran; T Pham; Xiaobo Zhou

doi:10.1109/ISPACS.2005.1595447

Cell Phase Identification Using Fuzzy Gaussian Mixture Models

Dat Tran, T Pham, Xiaobo Zhou

Information Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

2 Citations (Scopus)

52 Downloads (Pure)

Abstract

Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method

Original language	English
Title of host publication	Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems
Editors	K.N Ngan, W.C Siu
Place of Publication	Hong Kong
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	465-468
Number of pages	4
ISBN (Print)	0780392663
DOIs	https://doi.org/10.1109/ISPACS.2005.1595447
Publication status	Published - 2005
Event	International Symposium on Intelligent Signal Processing and Communication Systems - , Hong Kong Duration: 13 Dec 2005 → 16 Dec 2005

Conference

Conference	International Symposium on Intelligent Signal Processing and Communication Systems
Country/Territory	Hong Kong
Period	13/12/05 → 16/12/05

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10.1109/ISPACS.2005.1595447

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Cite this

@inproceedings{3b0b3ef1f5d641e1aad18c809fd4cb84,

title = "Cell Phase Identification Using Fuzzy Gaussian Mixture Models",

abstract = "Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method",

author = "Dat Tran and T Pham and Xiaobo Zhou",

year = "2005",

doi = "10.1109/ISPACS.2005.1595447",

language = "English",

isbn = "0780392663 ",

pages = "465--468",

editor = "K.N Ngan and W.C Siu",

booktitle = "Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "International Symposium on Intelligent Signal Processing and Communication Systems ; Conference date: 13-12-2005 Through 16-12-2005",

}

Tran, D, Pham, T & Zhou, X 2005, Cell Phase Identification Using Fuzzy Gaussian Mixture Models. in KN Ngan & WC Siu (eds), Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, Institute of Electrical and Electronics Engineers, Hong Kong, pp. 465-468, International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong, 13/12/05. https://doi.org/10.1109/ISPACS.2005.1595447

Cell Phase Identification Using Fuzzy Gaussian Mixture Models. / Tran, Dat; Pham, T; Zhou, Xiaobo.
Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems. ed. / K.N Ngan; W.C Siu. Hong Kong: IEEE, Institute of Electrical and Electronics Engineers, 2005. p. 465-468.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Cell Phase Identification Using Fuzzy Gaussian Mixture Models

AU - Tran, Dat

AU - Pham, T

AU - Zhou, Xiaobo

PY - 2005

Y1 - 2005

N2 - Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method

AB - Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method

U2 - 10.1109/ISPACS.2005.1595447

DO - 10.1109/ISPACS.2005.1595447

M3 - Conference contribution

SN - 0780392663

SP - 465

EP - 468

BT - Proceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems

A2 - Ngan, K.N

A2 - Siu, W.C

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Hong Kong

T2 - International Symposium on Intelligent Signal Processing and Communication Systems

Y2 - 13 December 2005 through 16 December 2005

ER -

Cell Phase Identification Using Fuzzy Gaussian Mixture Models

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