Novel cell segmentation and online learning algorithms for cell phase identification in automated time-lapse microscopy

Meng Wang, Xiaobo Zhou, Fuhai Li, Jeremy Huckins, Randy W. King, Stephen T. Wong

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

16 Scopus citations

Abstract

Automated identification of cell cycle phases captured via fluorescent microscopy technique is very important for cell cycle understanding and drug discovery. In this paper, we propose a novel cell detection method that utilizes both the intensity and shape information of cell to improve the segmentation quality. In contrast to conventional off-line learning algorithms for classifcation, our study necessitates the on-line adaptivity to accommodate the ever-changing experimental conditions. An Online Support Vector Classifier (OSVC) is thus proposed, which features the removal of support vectors from the old model and assigning the new training examples with different weights according to their importance. Experimental results show the proposed system is effective for cell imaging segmentation and cell phase identification in time-lapse microscopy.

Original languageEnglish (US)
Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro - Proceedings
Pages65-68
Number of pages4
DOIs
StatePublished - Nov 27 2007
Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
Duration: Apr 12 2007Apr 15 2007

Other

Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
CountryUnited States
CityArlington, VA
Period4/12/074/15/07

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Medicine(all)

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