By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Ning Wei ; Fac. of Technol., Bielefeld Univ. ; Flaschel, E. ; Saalbach, A. ; Twellman, T.
more authors

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

Published in:

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Date of Conference:

17-18 Jan. 2006