TITLE:
Automated Dynamic Cellular Analysis in Time-Lapse Microscopy
AUTHORS:
Shuntaro Aotake, Chamidu Atupelage, Zicong Zhang, Kota Aoki, Hiroshi Nagahashi, Daisuke Kiga
KEYWORDS:
High Dimension Feature Analysis, Microscopic Cell Image, Cell Division Cycle Identification, Active Contour Model, K-Means Clustering
JOURNAL NAME:
Journal of Biosciences and Medicines,
Vol.4 No.3,
March
17,
2016
ABSTRACT: 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.