A novel cell tracking algorithm and continuous hidden Markov model for cell phase identification

Xiaobo Zhou, Jun Yang, Meng Wang, Stephen T. Wong

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

4 Scopus citations

Abstract

Time-lapse microscopy cell imaging is attracting more and more attentions due to its potential in achieving new and high throughput ways to conduct drug discovery and quantitative cellular studies. However, the lacking of effective automatic systems for studying a large population of cell nuclei is limiting the application of it. In this paper, we propose a novel Hybrid Merging algorithm for cell nuclei segmentation and propose a novel Favorite Matching phis Local Tree Matching algorithm to track dynamic behaviors of a large population of cell nuclei in time-lapse microscopy. And then we propose to identify the phases of cell nuclei using context information of tracks by continuous Hidden Markov Model Experimental results show the whole proposed system is very effective for time-lapse microscopy cell imaging segmentation, tracking and cell phase identification.

Original languageEnglish (US)
Title of host publication2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006 - Bethesda, MD, United States
Duration: Jul 13 2006Jul 14 2006

Other

Other2006 IEEE/NLM Life Science Systems and Applications Workshop, LiSA 2006
CountryUnited States
CityBethesda, MD
Period7/13/067/14/06

ASJC Scopus subject areas

  • Health(social science)
  • Assessment and Diagnosis
  • Medicine(all)
  • Health Information Management
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Signal Processing

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