Cell phase identification using fuzzy Gaussian mixture models
Tran, Dat, Pham, Tuan, and Zhou, Xiaobo (2005) Cell phase identification using fuzzy Gaussian mixture models. In: Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems , pp. 465-468. From: 2005 International Symposium on Intelligent Signal Processing and Communication Systems, 13 - 16 December 2005, Hong Kong.
PDF (Published Version)
- Published Version
Restricted to Repository staff only
Fuzzy Gaussian mixture modeling method is proposed in this paper for the computerized classification of cell nuclei in different mitotic phases. A mixture of Gaussian distributions was used to represent the cell data in multi-dimensional cell feature space. Gaussian parameters were estimated using fuzzy c-means estimation. The method was tested with the data set containing 379519 cells in 5 phases extracted from real image sequences recorded at every fifteen minutes with a time-lapse fluorescence microscopy. Experimental results have shown that the proposed method is more effective than the Gaussian mixture modeling method.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Date Deposited:||07 Nov 2010 23:55|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 100%|
|Citation Count from Scopus||