Biomedical Image Processing

Quantificational Cell Behavior Analysis


Measurement of the proliferative behaviors of cells in vitro is important to many biomedical applications ranging from basic biological research to advanced industrial applications, such as drug discovery, stem cell manufacturing, and tissue engineering. Critical to such measurement is the accurate counting and localization of occurrence of mitosis, or cell division, in a cell culture. For short-period, small-scale studies, it is possible to manually identify incidents of mitosis because mitotic cells in culture tend to retract, round up, and exhibit intensified surrounding halos under phase contrast illumination. However, the need for extended-time observation and the proliferation of high-throughput imaging have made automated image analysis mandatory. We works on the algorithm development for automated cell behavior analysis in the time-lapse microscopy image sequences.

Representative Publications:

  • Anan Liu, Kang Li, Takeo Kanade, “A Semi-Markov Model for Mitosis Segmentation in Time-Lapse Phase Contrast Microscopy Image Sequences of Stem Cell Populations”, IEEE Transactions on Medical Imaging, Vol. 31, No. 2, pp. 359-369, 2012.

  • Anan Liu, Tong Hao, Zan Gao, Yuting Su, Zhaoxuan Yang, “Sequential Sparse Representation for Mitotic Event Recognition”, Electronics Letters, Vol.49, No.14, pp.813-816, 2013.

  • Anan Liu, Kang Li, Takeo Kanade, "Mitosis Sequence Detection Using Hidden Conditional Random Fields", IEEE International Symposium on Biomedical Imaging 2010, Netherlands, 2010.

  • Anan Liu, Kang Li, Takeo Kanade, “Spatiotemporal Mitosis Event Detection in Time-Lapse Phase Contrast Microscopy Image Sequences,” ICME 2010.