NDLI: Reagent-free automatic cell viability determination using neural networks based machine vision and dark-field microscopy in Saccharomyces cerevisiae

Content Provider	IEEE Xplore Digital Library
Author	Ning Wei Flaschel, E. Saalbach, A. Twellman, T. Nattkemper, T.W.
Copyright Year	2005
Description	Author affiliation: Fac. of Technol., Bielefeld Univ. (Ning Wei; Flaschel, E.)
Abstract	Fermentation industries require in-situ real-time monitoring of cell viability during fermentation processes. For this purpose, reagent-free approaches are desired because they can be used for in situ analysis and reduce the system's complexity. We have developed an automatic way of determining cell viability via analysis of time-lapse image sequences taken by dark field microscopy without the aid of any additional reagents. The image processing is based on neural networks based machine vision, involving principal component analysis (PCA) to investigate the dynamic information of intracellular movements. In consequence, the essential features as the vital sign of the target cells are discovered. Viability predictions using the support vector machine (SVM) classifier have been done successfully on the datasets with different qualities. Accuracy up to above 90% has been obtained on the basis of image enhancement. Robustness of the system is proved by the results of the tests. The model organism we have used is Saccharomyces cerevisiae, however, this technique can promisingly be applied for the identification of cell viability of other organisms as well
Starting Page	6305
Ending Page	6308
File Size	369254
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1615939
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Machine vision Microscopy Principal component analysis Support vector machines Support vector machine classification Organisms Monitoring Image analysis Image sequence analysis yeast viability reagent free dark field time lapse image PCA SVM neural networks machine vision
Content Type	Text
Resource Type	Article

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