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Automated Dynamic Cellular Analysis in Time-Lapse Microscopy
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作者 Shuntaro Aotake Chamidu Atupelage +3 位作者 Zicong Zhang Kota Aoki Hiroshi Nagahashi Daisuke Kiga 《Journal of Biosciences and Medicines》 2016年第3期44-50,共7页
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-... 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. 展开更多
关键词 High Dimension Feature Analysis Microscopic Cell Image Cell Division cycle identification Active Contour Model K-Means Clustering
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