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pubmed-article:17989093pubmed:abstractTextAutomated 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.lld:pubmed
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pubmed-article:17989093pubmed:articleTitleNovel cell segmentation and online SVM for cell cycle phase identification in automated microscopy.lld:pubmed
pubmed-article:17989093pubmed:affiliationCenter for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, 3rd floor, 1249 Boylston, Boston, MA 02215, USA.lld:pubmed
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