JBM  Vol.4 No.3 , March 2016
Automated Dynamic Cellular Analysis in Time-Lapse Microscopy
Abstract: Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
Cite this paper: Aotake, S. , Atupelage, C. , Zhang, Z. , Aoki, K. , Nagahashi, H. and Kiga, D. (2016) Automated Dynamic Cellular Analysis in Time-Lapse Microscopy. Journal of Biosciences and Medicines, 4, 44-50. doi: 10.4236/jbm.2016.43008.

[1]   Chen, X. and Wong, S.T.C. (2005) Automated Dynamic Cellular Analysis in High Throughput Drug Screens. IEEE International Symposium on Circuits and System, Kobe, 23-26 May 2005, 5, 4229-4232.

[2]   Nobuyuki, O. (1980) An Automatic Threshold Selection Method Based on Discriminant and Least Squares Criteria. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, J63-D, 349-356.

[3]   Xiaobo, C., Xiaobo, Z., Stephen, T.C. and Wong (2006) Auto-mated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-Lapse Microscopy. IEEE Transactions on Biomedical Engineering, 53, 762-766.

[4]   Petra, P., Horst, P. and Bernd, M. (2002) Mining Knowledge for HEp-2 Cell Image Classification. Artificial Intelligence in Medicine, 26, 161-173.

[5]   Loris, N. and Alessandra, L. (2008) A Reliable Method for Cell Phenotype Image Classification. Artificial Intelligence in Medicine, 43, 87-97.

[6]   Nicholas, A.H., Radosav, S.P., Kelly, H. and Rohan, D.T. (2007) Fast Automated Cell Phenotype Image Classification. BMC Bioinformatics, 8, 110.

[7]   Loris, N., Alessandra, L., Lin, Y.-S., Hsu, C.-N. and Chung, C.L. (2010) Fusion of Systems for Automated Cell Phenotype Image Classification. Expert Systems with Applications, 37, 1556-1562.

[8]   Jitendra, M., Serge, B., Thomas, L. and Jianbo, S. (2001) Contour and Texture analysis for Image Segmentation. International Journal of Contour Vision, 43, 7-27.

[9]   Lrystian, M. and Cordelia, S. (2001) Indexing Based on Scale Inva-riant Interest Points. Computer Vision ICCV Proceedings of the Eighth IEEE International Conference, 7-14 July 2001, 1, 525-531.

[10]   Chan, T.F. and Vese, L.A. (2001) Active Contours without Edges. IEEE Transactions on Image Processing, 10, 266- 277.

[11]   Pascal, G. (2012) Chan-Vese Segmentation. Image Processing On Line, 2, 214-224.