Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy

Meng Wang, Xiaobo Zhou, Fuhai Li, Jeremy Huckins, Randall W. King, Stephen T.C. Wong

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


Motivation: Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line learning algorithms, an Online Support Vector Classifier (OSVC) is thus proposed, which removes support vectors from the old model and assigns new training examples weighted according to their importance to accommodate the ever-changing experimental conditions. Results: We image three cell lines using fluorescent microscopy under different experiment conditions, including one treated with taxol. Then, we segment and classify the cell types into interphase, prophase, metaphase and anaphase. Experimental results show the effectiveness of the proposed system in image segmentation and cell phase identification.

Original languageEnglish (US)
Pages (from-to)94-101
Number of pages8
Issue number1
StatePublished - Jan 1 2008

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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