TY - JOUR
T1 - Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy
AU - Wang, Meng
AU - Zhou, Xiaobo
AU - Li, Fuhai
AU - Huckins, Jeremy
AU - King, Randall W.
AU - Wong, Stephen T.C.
N1 - Funding Information:
This research is funded by an NIH R01 LM008696 Grant.
PY - 2008/1/1
Y1 - 2008/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=37549026179&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btm530
DO - 10.1093/bioinformatics/btm530
M3 - Article
C2 - 17989093
AN - SCOPUS:37549026179
SN - 1367-4803
VL - 24
SP - 94
EP - 101
JO - Bioinformatics
JF - Bioinformatics
IS - 1
ER -