TY - GEN
T1 - Identification of cell-cycle phases using neural network and steerable filter features
AU - Yang, Xiaodong
AU - Li, Houqiang
AU - Zhou, Xiaobo
AU - Wong, Stephen T C
PY - 2006
Y1 - 2006
N2 - In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3].
AB - In this paper, we aim to address the cell phase identification problem, and two important aspects, the feature extraction methods and the classifier design, are discussed. In our study, we first propose extracting high frequency information of different orientations using Steerable filters. Next, we employ a multi-layer neural network using the back-propagation algorithm to replace K-Nearest Neighbor (KNN) classifier which has been implemented in the Cellular Image Quantitator (CELLIQ) system [3]. Experimental results provide a comparison between the proposed steerable filter features and existing regular features which have been used in published papers [3, 5]. From the comparison, it can be concluded that Steerable filter features can effectively represent the cells in different phases and improve the classification accuracy. Neural network also has a better performance than KNN currently deployed in CELLIQ system [3].
UR - http://www.scopus.com/inward/record.url?scp=33745888395&partnerID=8YFLogxK
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U2 - 10.1007/11760191_103
DO - 10.1007/11760191_103
M3 - Conference contribution
AN - SCOPUS:33745888395
SN - 3540344829
SN - 9783540344827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 702
EP - 709
BT - Advances in Neural Networks - ISNN 2006
PB - Springer-Verlag
T2 - 3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks
Y2 - 28 May 2006 through 1 June 2006
ER -