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Textural feature based cell segmentation method for isolating Chinese hamster ovary cells
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- AOTAKE Shuntaro
- Department of Computational Int elligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering , Tokyo Institute of Technology
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- ATUPELAGE Chamidu
- Imaging Science and Engineering Laboratory , Tokyo Institute of Technology
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- AOKI Kota
- Imaging Science and Engineering Laboratory , Tokyo Institute of Technology
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- NAGAHASHI Hirosi
- Imaging Science and Engineering Laboratory , Tokyo Institute of Technology
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- KIGA Daisuke
- Department of Computational Int elligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering , Tokyo Institute of Technology
Bibliographic Information
- Other Title
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- 顕微鏡画像における テクスチャ解析 を用いた細胞領域抽出 に関する研究
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Description
Analysis of cellular behavior is signif icant for studying cell cycle and detecting anti cancer drugs.Isolating individual cells in confocal microscopic images of non stained live cell cultures is very difficult process. Because these images do not have adequate textural variations and the cell s are often overlap in consecutive focal planes. Manual cell segmentation requires massive labor and it is a time consuming process. This paper describes a automated cell segmentation method for localizing the individual cells of Chinese hamster ovary cell culture. K means clustering method is used to extract the cellular regions from the background. Subsequently, level set method is used to determine the boundaries of individual cells. The result indicates the significance of the proposed method as it appr oximates the boundaries of cells, which are placed very close r or overlapped in each other.
Journal
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- Proceedings of the Annual Conference of the Institute of Image Electronics Engineers of Japan
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Proceedings of the Annual Conference of the Institute of Image Electronics Engineers of Japan 43 (0), 20-20, 2015
The Institute of Image Electronics Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390289225020595712
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- NII Article ID
- 130008081263
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- ISSN
- 24364398
- 24364371
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed