Identification of cell-cycle phases using neural network and steerable filter features

Authors:
Xiaodong Yang;Houqiang Li;Xiaobo Zhou;Stephen T. C. Wong
Affiliations:
Department of Electronic Engineering and Information Science, University of Science and Technology of China;Department of Electronic Engineering and Information Science, University of Science and Technology of China;Center for Bioinformatics, HCNR, Harvard Medical School, Boston, MA;Center for Bioinformatics, HCNR, Harvard Medical School, Boston, MA
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

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].