Efficient Cell Tracking Method for Automatic Analysis of Cellular Sequences

세포동영상의 자동분석을 위한 효율적인 세포추적방법

  • 한찬희 (한밭대학교 정보통신전문대학원) ;
  • 송인환 (한밭대학교 정보통신전문대학원) ;
  • 이시웅 (한밭대학교 정보통신전문대학원)
  • Received : 2011.01.14
  • Accepted : 2011.03.08
  • Published : 2011.05.28


The tracking and analysis of cell activities in time-lapse sequences plays an important role in understanding complex biological processes such as the spread of the tumor, an invasion of the virus, the wound recovery and the cell division. For automatic tracking of cells, the tasks such as the cell detection at each frame, the investigation of the correspondence between cells in previous and current frames, the identification of the cell division and the recognition of new cells must be performed. This paper proposes an automatic cell tracking algorithm. In the first frame, the marker of each cell is extracted using the feature vector obtained by the analysis of cellular regions, and then the watershed algorithm is applied using the extracted markers to produce the cell segmentation. In subsequent frames, the segmentation results of the previous frame are incorporated in the segmentation process for the current frame. A combined criterion of geometric and intensity property of each cell region is used for the proper association between previous and current cells to obtain correct cell tracking. Simulation results show that the proposed method improves the tracking performance compared to the tracking method in Cellprofiler (the software package for automatic analysis of bioimages).


Cell Tracking;Bioinformatics;Automatic Analysis of Bioimages


Supported by : 한국연구재단


  1. E. Meijering, I. Smal, and G. Danuser, "Tracking in Molecular Bioimaging," IEEE Signal Processing Magazine, Vol.23, No.3, pp.46-53, 2006(5).
  2. K. Li, E. D. Miller, M. Chen, T. Kanade, L. E. Weiss, and P. G. Campbell, "Cell population tracking and lineage construction with spatiotemporal context," Medical Image Analysis, Vol.12, pp.546-566, 2008.
  3. A. E. Carpenter, "Cellprofiler: Image analysis for high throughput microscopy," [Online]. Available :
  4. A. Genovesio, T. Liedl, V. Emiliani, W. J. Parak, M. Coppey-Moisan, and J. C. Olivo-Marin, "Multiple particle tracking in 3-D+t microscopy: Method and application to the tracking of endocytosed quantum dots," IEEE Transactions on Image Processing, Vol.15, No.5, pp.1062-1070, 2006.
  5. L. Zhang, H. Xiong, K. Zhang, and X. Zhou, "Graph theory application in cell nuleus segmentation, tracking and identification," Proc. of the IEEE Conference on Bioinformatics and Bioengineering, pp.26-232, 2007.
  6. I. Smal, K. Dragestein, N. Galjart, W. Niessen, and E. Meijering, "Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: Application to microtubule growth analysis," IEEE Transactions on Medical Imaging, Vol.27, No.6, pp.789-804, 2008.
  7. S. K. Nath, B. Filiz, and K. Palaniappan, "Robust tracking of migrating cells using four-color level set segmentation," Lecture Notes in Computer Science (LCNS), Vol.4179, pp.920-932, 2008.
  8. K. Li, E.D. Miller, M. Chen, T. Kanade, L. E. Weiss, and P. G. Campbell, "Cell population tracking and lineage construction with spatiotemporal context," Medical Image Analysis, Vol.12, pp.546-566, 2008.
  9. D. P. Mukherjee, N. Ray, and S. T. Acton, "Level set analysis for leukocyte detection and tracking," IEEE Transactions on Image Processing, Vol.13, No4, pp.562-572, 2004.
  10. X. Yang, H. Li, and X. Zhou, "Nuclei segmentation using marker controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy," IEEE Transactions on Circuits and Systems, Vol.53, No.11, pp.2405-2414, 2006.
  11. L. Vincent and P. Soille, "Watersheds in digital spaces: an efficient algorithm based on immersion simulations," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.13, No.6, pp.583-598, 1991(6).