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Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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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].

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References

  1. Yang, X., Li, H., Zhou, X., Wong, S.T.C.: Automated Segmentation and Tracking of Cells in Time-Lapse Microscopy Using Watershed and Mean Shift. In: International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong (In Press)

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  2. Yang, X., Li, H., Zhou, X., Wong, S.T.C.: Nuclei Segmentation Using Marker-controlled Watershed, Tracking Using Mean-shift and Kalman Filter in Time-Lapse Microscopy. Submitted to IEEE Transactions on Circuits and Systems (2006)

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  3. Chen, X., Zhou, X., Wong, S.T.C.: Automated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-Lapse Microscopy. IEEE Transactions on Biomedical Engineering (2006) (Accepted for Publication)

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering and Information Science, University of Science and Technology of China,  

    Xiaodong Yang & Houqiang Li

  2. Center for Bioinformatics, HCNR, Harvard Medical School, Boston, MA, 02115, USA

    Xiaobo Zhou & Stephen T. C. Wong

Authors
  1. Xiaodong Yang
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  2. Houqiang Li
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  3. Xiaobo Zhou
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  4. Stephen T. C. Wong
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Yang, X., Li, H., Zhou, X., Wong, S.T.C. (2006). Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_103

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  • DOI: https://doi.org/10.1007/11760191_103

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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