Image Classification by Fusion for High-Content Cell-Cycle Screening

  • Tuan D. Pham
  • Dat T. Tran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


We present a fuzzy fusion approach for combining cell-phase identification results obtained from multiple classifiers. This approach can improve the classification rates and allows the task of high-content cell-cycle screening more effective for biomedical research in the study of structures and functions of cells and molecules. Conventionally such study requires the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. The proposed method has been used to combine the results from three classifiers, and the combined result is superior to any of the results obtained from a single classifier.


Cell Phase Vector Quantization Fuzzy Measure Fuzzy Entropy High Content Screening 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tuan D. Pham
    • 1
    • 2
  • Dat T. Tran
    • 3
  1. 1.Bioinformatics Applications Research Centre 
  2. 2.School of Information TechnologyJames Cook UniversityTownsvilleAustralia
  3. 3.School of Information Sciences and EngineeringUniversity of CanberraAustralia

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