An effective system for optical microscopy cell image segmentation, tracking and cell phase identification

Jun Yan, Xiaobo Zhou, Qiong Yang, Ning Liu, Qiansheng Cheng, Stephen T C Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous Hidden Markov Model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages1917-1920
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Biological systems
  • Biomedical image processing
  • Biomedical signal processing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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