Gaussian mixture and Markov models for cell-phase classification in microscopic imaging
Pham, Tuan, and Tran, Dat T. (2006) Gaussian mixture and Markov models for cell-phase classification in microscopic imaging. Proceedings of the 2006 IEEE International Conference on System of Systems Engineering. 2006 IEEE International Conference on System of Systems Engineering , 24-26 April 2006, Los Angeles, CA, USA , pp. 328-333.
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Studies of drug effects on cancer cells are performed through measuring cell cycle progression such as inter phase, prophase, metaphase and anaphase in individual cells. Such studies require the processing and analysis of huge amounts of image data. Manual image analysis is very time consuming thus costly, potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We considered applying several versions of Gaussian mixture and Markov models for automating the classification of cell nuclei in different mitotic phases recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluorescence microscopy. The experimental results have shown that the proposed methods are effective and have potential for higher performance.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||bioimaging, classification, identification, Gaussian mixture models, Markov models, cellular imaging|
|SEO Codes:||92 HEALTH > 9202 Health and Support Services > 920203 Diagnostic Methods @ 34%|
92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920412 Preventive Medicine @ 33%
92 HEALTH > 9201 Clinical Health (Organs, Diseases and Abnormal Conditions) > 920102 Cancer and Related Disorders @ 33%
|Deposited On:||24 Nov 2009 14:53|
|Last Modified:||02 Nov 2012 09:12|
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