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Comparison of automated feature extraction methods for image based screening of cancer cells
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Image based screening is an important tool used in research for development of drugs to fight cancer. Phase contrast video microscopy - a cheap and fast image screening technology - enables a rapid generation of large amounts of data, which requires a fast method for analysis of this data. As videos contain a lot of redundant information, the difficulty is to extract usable information in form of features from the videos, by compressing available information, or filter out redundant data. In this thesis, the problem is approached in an experimental fashion where three different methods have been devised and tested, to evaluate different ways to automatically extract features from phase contrast microscopy videos containing cultured cancer cells. The three methods considered are, in order: an adaptive linear filter, an on-line clustering algorithm, and an artificial neural network. The ambition is that outputs from these methods can create time-varying histograms of features that can be used in further mathematical modeling of cell dynamics. It is concluded that, while the results of the first method is not impressive and can be dismissed, the remaining two are more promising and are able to successfully extract features automatically and aggregate them into time-varying histograms.

Place, publisher, year, edition, pages
2012. , 36 p.
UPTEC F, ISSN 1401-5757 ; 11068
Keyword [en]
image screening, pattern recognition, cancer, microscopy, image analysis
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-167602OAI: diva2:486599
Educational program
Master Programme in Engineering Physics
Available from: 2012-02-02 Created: 2012-01-30 Last updated: 2012-02-02Bibliographically approved

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Brennan, Michael
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