Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Classification of Tumor cells in Phase-contrast Microscopy Image using Fourier Descriptor

위상차 현미경 영상 내 푸리에 묘사자를 이용한 암세포 형태별 분류

  • Kang, Mi-Sun (Dept. of Computer Science and Engineering, Ewha Womans University) ;
  • Lee, Jeong-Eom (Dept. of Computer Science and Engineering, Ewha Womans University) ;
  • Kim, Hye-Ryun (Dept. of Computer Science and Engineering, Ewha Womans University) ;
  • Kim, Myoung-Hee (Dept. of Computer Science and Engineering, Ewha Womans University)
  • 강미선 (이화여자대학교 컴퓨터공학과) ;
  • 이정엄 (이화여자대학교 컴퓨터공학과) ;
  • 김혜련 (이화여자대학교 컴퓨터공학과) ;
  • 김명희 (이화여자대학교 컴퓨터공학과)
  • Received : 2012.07.11
  • Accepted : 2012.11.17
  • Published : 2012.12.31
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Abstract

Tumor cell morphology is closely related to its migratory behaviors. An active tumor cell has a highly irregular shape, whereas a spherical cell is inactive. Thus, quantitative analysis of cell features is crucial to determine tumor malignancy or to test the efficacy of anticancer treatment. We use 3D time-lapse phase-contrast microscopy to analyze single cell morphology because it enables to observe long-term activity of living cells without photobleaching and phototoxicity, which is common in other fluorescence-labeled microscopy. Despite this advantage, there are image-level drawbacks to phase-contrast microscopy, such as local light effect and contrast interference ring. Therefore, we first corrected for non-uniform illumination artifacts and then we use intensity distribution information to detect cell boundary. In phase contrast microscopy image, cell is normally appeared as dark region surrounded by bright halo ring. Due to halo artifact is minimal around the cell body and has non-symmetric diffusion pattern, we calculate cross sectional plane which intersects center of each cell and orthogonal to first principal axis. Then, we extract dark cell region by analyzing intensity profile curve considering local bright peak as halo area. Finally, we calculated the Fourier descriptor that morphological characteristics of cell to classify tumor cells into active and inactive groups. We validated classification accuracy by comparing our findings with manually obtained results.

Keywords

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