Subspace Vector Quantization and Markov Modeling for Cell Phase Classification

  • Dat Tran
  • Tuan Pham
  • Xiaobo Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)


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.


Fluorescence microscopic imaging cell phase classification subspace vector quantization fuzzy c-means fuzzy entropy Markov chain 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dat Tran
    • 1
  • Tuan Pham
    • 2
  • Xiaobo Zhou
    • 3
  1. 1.Faculty of Information Sciences and EngineeringUniversity of CanberraAustralia
  2. 2.Bioinformatics Applications Research CentreJames Cook UniversityAustralia
  3. 3.HCNR-Center for BioinformaticsHarvard Medical SchoolBostonUSA

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