KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Development of HCS(High Contents Screening) Software Using Open Source Library

오픈 소스 라이브러리를 활용한 HCS 소프트웨어 개발

  • 나예지 (순천향대학교 의료IT공학과) ;
  • 호종갑 (순천향대학교 의료IT공학과) ;
  • 이상준 (선문대학교 기계ICT융합공학부) ;
  • 민세동 (순천향대학교 의료IT공학과)
  • Received : 2016.05.02
  • Accepted : 2016.05.17
  • Published : 2016.06.30
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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%.

생물정보학분야에서 현미경을 통해 얻은 세포 영상은 생물학적 정보를 얻기 위한 중요한 지표이다. 연구자들은 영상을 육안으로 분석하기 때문에 분석에 많은 시간과 고도의 집중력이 요구된다. 게다가 연구자의 주관적 관점이 분석에 개입되어 결과를 객관적으로 정량화하는데 어려움이 있다. 따라서 본 연구에서는 OpenCV 라이브러리를 이용하여 세포의 자동 분석을 위한 HCS(High Content Screen) 알고리즘을 개발하였다. HCS 알고리즘은 이미지 전처리 과정, 세포 계수, 세포 주기와 분열지수 분석 기능을 포함한다. 본 연구에서는 공초점 레이저 현미경을 통해 얻은 위암세포(MKN-28) 영상을 분석에 사용하였으며, 성능 평가를 위해 세포영상 분석 프로그램인 ImageJ와 전문 연구원의 세포 계수 분석결과를 비교하였다. 실험 결과 HCS 알고리즘의 평균 정확성이 99.7%로 나타났다.

Keywords

References

  1. Sung-on Lee, "Example of Fusion between BT technology in enhanced IT technology and biological microscope IT technology," Journal of the KSME, Vol.52, No.11, pp.52-55, 2012.
  2. R. Juskaitis, N. P. Rea, and Wilson Tony, "Semiconductor laser confocal microscopy," Applied Optics, Vol.33, No.4, pp.578-584, 1994. https://doi.org/10.1364/AO.33.000578
  3. Benjamin A. Flusberg et al., "In vivo brain imaging using a portable 3.9? gram two-photon fluorescence microendoscope," Optics Letters, Vol.30, No.17, pp.2272-2274, 2005. https://doi.org/10.1364/OL.30.002272
  4. Juergen C. Jung et al., "In vivo mammalian brain imaging using one-and two-photon fluorescence microendoscopy," Journal of Neurophysiology, Vol.92, No.5, pp.3121-3133, 2004. https://doi.org/10.1152/jn.00234.2004
  5. Ingo Krohne, et al., "New method for confocal microscopy and its endoscopic application," European Conference on Biomedical Optics 2003. International Society for Optics and Photonics, 2003.
  6. Joachim Knittel et al., "Endoscope-compatible confocal microscope using a gradient index-lens system," Optics Communications, Vol.188, No.5, pp.267-273, 2001. https://doi.org/10.1016/S0030-4018(00)01164-0
  7. Anne E. Carpenter et al., "CellProfiler: image analysis software for identifying and quantifying cell phenotypes," Genome Biology, Vol.7, No.10, R100, 2006. https://doi.org/10.1186/gb-2006-7-10-r100
  8. Graeme Milligan, "High-content assays for ligand regulation of G-protein-coupled receptors," Drug Discovery Today, Vol.8, No.13, pp.579-585, 2003. https://doi.org/10.1016/S1359-6446(03)02738-7
  9. J. Ralph, "A multi-faceted approach to the advancement of cell-based drug discovery," Drug Discovery, Vol.5, p.43, 2004.
  10. T. Wilson, "Optical sectioning in confocal fluorescent microscopes," Journal of Microscopy, Vol.154, No.2, pp.143-156, 1989. https://doi.org/10.1111/j.1365-2818.1989.tb00577.x
  11. W. Niblack, "An Introduction to Image Processing," Englewood Cliffs, NJ: Prentice-Hall, pp.115-116, 1986.
  12. L. Vincent and P. Soille, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, No.6, pp.583-598. https://doi.org/10.1109/34.87344
  13. J. Canny, "A Computational Approach to Edge Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.8, pp.679-698, 1986.
  14. Paul S. Heckbert, "A seed fill algorithm," in Graphics gems, Academic Press Professional, Inc., 1990.
  15. H. Freeman, "On the encoding of arbitrary geometric configurations," IRE Transactions on Electronic Computers, Vol.EC-10, pp.260-268, 1961. https://doi.org/10.1109/TEC.1961.5219197
  16. Yeji. Na et al., "Basic research of foreign HCS equipment compatible with the cell analysis system," The Korean Institute of Electrical Engineers, 46th Conference(2015), pp. 1415-1416.
  17. Jorn Rittweger et al., "Adjusting for the partial volume effect in cortical bone analyses of pQCT images," Journal of Musculoskeletal and Neuronal Interactions, Vol.4, No.4, p.436, 2004.
  18. Martin J. Tovee et al., "Measurement of body size and shape perception in eating disordered and control observers using body shape software," British Journal of Psychology, Vol.94, No.4, pp.501-516, 2003. https://doi.org/10.1348/000712603322503060