%0 Journal Article
%T Automated Dynamic Cellular Analysis in Time-Lapse Microscopy
%A Shuntaro Aotake
%A Chamidu Atupelage
%A Zicong Zhang
%A Kota Aoki
%A Hiroshi Nagahashi
%A Daisuke Kiga
%J Journal of Biosciences and Medicines
%P 44-50
%@ 2327-509X
%D 2016
%I Scientific Research Publishing
%R 10.4236/jbm.2016.43008
%X <p class=\"MsoNormal\">
	<span lang=\"EN-US\">Analysis of cellular behavior
is significant for studying cell cycle and detecting anti-cancer drugs. It is a
very difficult task for image processing to isolate individual cells in
confocal microscopic images of non-stained live cell cultures. Because these
images do not have adequate textural variations. Manual cell segmentation
requires massive labor and is a time consuming process. This paper describes an
automated cell segmentation method for localizing the cells of Chinese hamster
ovary cell culture. Several kinds of high-dimensional feature descriptors,
K-means clustering method and Chan-Vese model-based level set are used to
extract the cellular regions. The region extracted are used to classify phases
in cell cycle. The segmentation results were experimentally assessed. As a
result, the proposed method proved to be significant for cell isolation. In the
evaluation experiments, we constructed a database of Chinese Hamster Ovary
Cell¡¯s microscopic images which includes various photographing environments
under the guidance of a biologist.</span>
</p>
%K High Dimension Feature Analysis
%K Microscopic Cell Image
%K Cell Division Cycle Identification
%K Active Contour Model
%K K-Means Clustering
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=64662