Subspace Vector Quantization and Markov Modeling for cell Phase Classification

Dat Tran, T Pham, Xiaobo Zhou

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

    Abstract

    Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science - Image Analysis and Recognition
    EditorsAurelio Campilho, Mohamed Kamel
    Place of PublicationGermany
    PublisherSpringer
    Pages844-853
    Number of pages10
    Volume5112
    ISBN (Print)9783540698111
    Publication statusPublished - 2008
    Event5th International Conference, ICIAR 2008 - , Portugal
    Duration: 25 Jun 2008 → …

    Conference

    Conference5th International Conference, ICIAR 2008
    CountryPortugal
    Period25/06/08 → …

    Fingerprint

    Vector quantization
    Entropy
    Fluorescence

    Cite this

    Tran, D., Pham, T., & Zhou, X. (2008). Subspace Vector Quantization and Markov Modeling for cell Phase Classification. In A. Campilho, & M. Kamel (Eds.), Lecture Notes in Computer Science - Image Analysis and Recognition (Vol. 5112, pp. 844-853). Germany: Springer.
    Tran, Dat ; Pham, T ; Zhou, Xiaobo. / Subspace Vector Quantization and Markov Modeling for cell Phase Classification. Lecture Notes in Computer Science - Image Analysis and Recognition. editor / Aurelio Campilho ; Mohamed Kamel. Vol. 5112 Germany : Springer, 2008. pp. 844-853
    @inproceedings{d2aad9ea13534b78919910b6495ddcab,
    title = "Subspace Vector Quantization and Markov Modeling for cell Phase Classification",
    abstract = "Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.",
    author = "Dat Tran and T Pham and Xiaobo Zhou",
    year = "2008",
    language = "English",
    isbn = "9783540698111",
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    pages = "844--853",
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    Tran, D, Pham, T & Zhou, X 2008, Subspace Vector Quantization and Markov Modeling for cell Phase Classification. in A Campilho & M Kamel (eds), Lecture Notes in Computer Science - Image Analysis and Recognition. vol. 5112, Springer, Germany, pp. 844-853, 5th International Conference, ICIAR 2008, Portugal, 25/06/08.

    Subspace Vector Quantization and Markov Modeling for cell Phase Classification. / Tran, Dat; Pham, T; Zhou, Xiaobo.

    Lecture Notes in Computer Science - Image Analysis and Recognition. ed. / Aurelio Campilho; Mohamed Kamel. Vol. 5112 Germany : Springer, 2008. p. 844-853.

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

    TY - GEN

    T1 - Subspace Vector Quantization and Markov Modeling for cell Phase Classification

    AU - Tran, Dat

    AU - Pham, T

    AU - Zhou, Xiaobo

    PY - 2008

    Y1 - 2008

    N2 - Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.

    AB - Vector quantization (VQ) and Markov modeling methods for cellular phase classification using time-lapse fluorescence microscopic image sequences have been proposed in our previous work. However the VQ method is not always effective because cell features are treated equally although their importance may not be the same. We propose a subspace VQ method to overcome this drawback. The proposed method can automatically weight cell features based on their importance in modeling. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering are proposed. Experimental results show that the proposed method can improve the cell phase classification rates.

    M3 - Conference contribution

    SN - 9783540698111

    VL - 5112

    SP - 844

    EP - 853

    BT - Lecture Notes in Computer Science - Image Analysis and Recognition

    A2 - Campilho, Aurelio

    A2 - Kamel, Mohamed

    PB - Springer

    CY - Germany

    ER -

    Tran D, Pham T, Zhou X. Subspace Vector Quantization and Markov Modeling for cell Phase Classification. In Campilho A, Kamel M, editors, Lecture Notes in Computer Science - Image Analysis and Recognition. Vol. 5112. Germany: Springer. 2008. p. 844-853