A novel cell segmentation method and cell phase identification using Markov model

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

Research output: Contribution to journalArticle

60 Scopus citations

Abstract

Optical microscopy is becoming an important technique in drug discovery and life science research. The approaches used to analyze optical microscopy images are generally classified into two categories: automatic and manual approaches. However, the existing automatic systems are rather limited in dealing with large volume of time-lapse microscopy images because of the complexity of cell behaviors and morphological variance. On the other hand, manual approaches are very time-consuming. In this paper, we propose an effective automated, quantitative analysis system that can be used to segment, track, and quantize cell cycle behaviors of a large population of cells nuclei effectively and efficiently. We use adaptive thresholding and watershed algorithm for cell nuclei segmentation followed by a fragment merging method that combines two scoring models based on trend and no trend features. Using the context information of time-lapse data, the phases of cell nuclei are identified accurately via a Markov model. Experimental results show that the proposed system is effective for nuclei segmentation and phase identification.

Original languageEnglish
Pages (from-to)152-157
Number of pages6
JournalIEEE Transactions on Information Technology in Biomedicine
Volume13
Issue number2
DOIs
StatePublished - Apr 3 2009
Externally publishedYes

Keywords

  • Cell phase identification
  • Continuous Markov model
  • Nuclei segmentation
  • Time-lapse fluorescence microscopy
  • Tracking

Fingerprint Dive into the research topics of 'A novel cell segmentation method and cell phase identification using Markov model'. Together they form a unique fingerprint.

  • Cite this