Development of HCS(High Contents Screening) SoftwareUsing Open Source Library


KIPS Transactions on Software and Data Engineering, Vol. 5, No. 6, pp. 267-272, Jun. 2016
10.3745/KTSDE.2016.5.6.267,   PDF Download:
Keywords: cell segmentation, Cell Cycle, Cell Counting, Bio-Image Automated Analysis
Abstract

Microscope cell image is an important indicator for obtaining the biological information in a bio-informatics fields. Since human observers have been examining the cell image with microscope, a lot of time and high concentration are required to analyze cell images. Furthermore, It is difficult for the human eye to quantify objectively features in cell images. In this study, we developed HCS algorithm for automatic analysis of cell image using an OpenCV library. HCS algorithm contains the cell image preprocessing, cell counting, cell cycle and mitotic index analysis algorithm. We used human cancer cell (MKN-28) obtained by the confocal laser microscope for image analysis. We compare the value of cell counting to imageJ and to a professional observer to evaluate our algorithm performance. The experimental results showed that the average accuracy of our algorithm is 99.7%.


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Cite this article
[IEEE Style]
Y. J. Na, J. G. Ho, S. J. Lee, S. D. Min, "Development of HCS(High Contents Screening) SoftwareUsing Open Source Library," KIPS Transactions on Software and Data Engineering, vol. 5, no. 6, pp. 267-272, 2016. DOI: 10.3745/KTSDE.2016.5.6.267.

[ACM Style]
Ye Ji Na, Jong Gab Ho, Sang Joon Lee, and Se Dong Min. 2016. Development of HCS(High Contents Screening) SoftwareUsing Open Source Library. KIPS Transactions on Software and Data Engineering, 5, 6, (2016), 267-272. DOI: 10.3745/KTSDE.2016.5.6.267.