Identification of Cell-Cycle Phases Using Neural Network and Steerable Filter Features
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 . 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 .
KeywordsClassification Accuracy Hide Unit Zernike Moment Gabor Wavelet Regular Feature
Unable to display preview. Download preview PDF.
- 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)Google Scholar
- 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)Google Scholar
- 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)Google Scholar
- 5.Huang, K., Murphy, R.F.: Boosting Accuracy of Automated Classification of Fluorescence Microscope Images for Location Proteomics. BMC Bioinformatics 78(5) (2004)Google Scholar
- 6.Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)Google Scholar