Relaxation Labeling for Cell-phase Identification

Dat Tran, T Pham

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    Gaussian mixture model (GMM) is used in cell phase identification to model the distribution of cell feature vectors. The model parameters, which are mean vectors, covariance matrices and mixture weights, are trained in an unsupervised learning method using the expectation maximization algorithm. Experiments have shown that the GMM is an effective method capable of achieving high identification rate. However, the GMM approach is not always effective because of ambiguity inherently existing in the cell phase data. To enhance the effectiveness of the GMM for solving this specific problem, the relaxation labeling (RL) is proposed to be used with the GMM. The RL algorithm is a parallel algorithm that updates the probabilities of cell phases by using correlation or mutual information between cell phases to reduce uncertainty among GMMs having overlapping properties.
    Original languageEnglish
    Title of host publicationProceedings: 2006 IEEE International Conference on Systems, Man and Cybernetics
    EditorsBing-Fei Wu, Chung-Cheng Chiu
    Place of PublicationTaiwan
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages6
    ISBN (Print)1424400996
    Publication statusPublished - 2006
    EventIEEE International Conference on Systems, Man, and Cybernetics - Taipei, Taiwan, Province of China
    Duration: 8 Oct 200611 Oct 2006


    ConferenceIEEE International Conference on Systems, Man, and Cybernetics
    Country/TerritoryTaiwan, Province of China


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