Novel nuclei segmentation and cell phase identification using Markov model

Fuhai Li, Xiaobo Zhou, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Optical microscopy is becoming an important technique in drug discovery and life science research. In this paper, we propose a fully automated system for quantifying, analyzing cell-cycle behaviors of cancer cells. We propose an adaptive thresholding and a cell detection algorithm for cell nuclei segmentation. A migration and size based tracking method is employed to reconstruct the traces of nuclei. Based on the context information, the phases of cell nuclei are identified using a Markov model. Experimental results show the proposed system is effective for nuclei segmentation and phase identification.

Original languageEnglish
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages96-103
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: Dec 17 2007Dec 19 2007

Publication series

NameAIP Conference Proceedings
Volume952
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2007 International Symposium on Computational Models for Life Sciences, CMLS '07
Country/TerritoryAustralia
CityGold Coast, QLD
Period12/17/0712/19/07

Keywords

  • Cell phase identification
  • Markov model
  • Nuclei segmentation
  • Tracking

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