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
2007 Computer applications in the biosciences : CABIOS  
Motivation: Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this paper, 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
more » ... new training examples weighted according to their importance to accommodate the ever-changing experimental conditions. Results: We image 3 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.
doi:10.1093/bioinformatics/btm530 pmid:17989093 fatcat:kxfbzqyjvbadhgj4urkzzfmgtu