Cell Phase Classification Using Markov and Gaussian Mixture Models

Dat Tran, T Pham

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy.
    Original languageEnglish
    Title of host publicationProceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing
    EditorsH Yan, J S Jin, Z Liu, D S Yeung
    Place of PublicationHong Kong
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages48-52
    Number of pages5
    ISBN (Print)962-442-278-8
    Publication statusPublished - 2005
    Event2005 Asia-Pacific Workshop on Visual Information Processing - , Hong Kong
    Duration: 11 Dec 200513 Dec 2005

    Conference

    Conference2005 Asia-Pacific Workshop on Visual Information Processing
    CountryHong Kong
    Period11/12/0513/12/05

    Fingerprint

    Fluorescence microscopy
    Feature extraction
    Cells

    Cite this

    Tran, D., & Pham, T. (2005). Cell Phase Classification Using Markov and Gaussian Mixture Models. In H. Yan, J. S. Jin, Z. Liu, & D. S. Yeung (Eds.), Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing (pp. 48-52). Hong Kong: IEEE, Institute of Electrical and Electronics Engineers.
    Tran, Dat ; Pham, T. / Cell Phase Classification Using Markov and Gaussian Mixture Models. Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing . editor / H Yan ; J S Jin ; Z Liu ; D S Yeung. Hong Kong : IEEE, Institute of Electrical and Electronics Engineers, 2005. pp. 48-52
    @inproceedings{c98c5c5cc2d44ca8b20dae8023088200,
    title = "Cell Phase Classification Using Markov and Gaussian Mixture Models",
    abstract = "We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy.",
    author = "Dat Tran and T Pham",
    year = "2005",
    language = "English",
    isbn = "962-442-278-8",
    pages = "48--52",
    editor = "H Yan and Jin, {J S} and Z Liu and Yeung, {D S}",
    booktitle = "Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    Tran, D & Pham, T 2005, Cell Phase Classification Using Markov and Gaussian Mixture Models. in H Yan, JS Jin, Z Liu & DS Yeung (eds), Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing . IEEE, Institute of Electrical and Electronics Engineers, Hong Kong, pp. 48-52, 2005 Asia-Pacific Workshop on Visual Information Processing, Hong Kong, 11/12/05.

    Cell Phase Classification Using Markov and Gaussian Mixture Models. / Tran, Dat; Pham, T.

    Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing . ed. / H Yan; J S Jin; Z Liu; D S Yeung. Hong Kong : IEEE, Institute of Electrical and Electronics Engineers, 2005. p. 48-52.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    TY - GEN

    T1 - Cell Phase Classification Using Markov and Gaussian Mixture Models

    AU - Tran, Dat

    AU - Pham, T

    PY - 2005

    Y1 - 2005

    N2 - We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy.

    AB - We present Gaussian mixture and Markov modelling methods for the computerized classification of cell nuclei in different mitotic phases. The methods were tested with the data set containing 379519 cells in 892 cell sequences for 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance with better cellular feature extraction strategy.

    M3 - Conference contribution

    SN - 962-442-278-8

    SP - 48

    EP - 52

    BT - Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing

    A2 - Yan, H

    A2 - Jin, J S

    A2 - Liu, Z

    A2 - Yeung, D S

    PB - IEEE, Institute of Electrical and Electronics Engineers

    CY - Hong Kong

    ER -

    Tran D, Pham T. Cell Phase Classification Using Markov and Gaussian Mixture Models. In Yan H, Jin JS, Liu Z, Yeung DS, editors, Proceedings of the 2005 Asia-Pacific Workshop on Visual Information Processing . Hong Kong: IEEE, Institute of Electrical and Electronics Engineers. 2005. p. 48-52