Tran, Dat T., and Pham, Tuan D. (2006) Relaxation labeling for cell phase identification. In: Proceedings of the 2006 IEEE International Conference on Systems, Man and Cybernetics (2) pp. 1275-1280. From: SMC'06 IEEE International Conference on Systems, Man and Cybernetics 2006, 8-11 October 2006, Taipei, Taiwan.

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Abstract

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.

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