A K-means segmentation method for finding 2-D object areas based on 3-D image stacks obtained by confocal microscopy

Annu Int Conf IEEE Eng Med Biol Soc. 2007:2007:5559-62. doi: 10.1109/IEMBS.2007.4353606.

Abstract

A segmentation method for three-dimensional image stacks obtained by confocal microscopy is proposed. The method can be used to find two-dimensional object areas based on an image stack. The segmentation method is based on K-means clustering, global thresholding, and mathematical morphology. As a case study, the proposed method is applied to 244 image stacks of the yeast Saccharomyces cerevisiae. Quantitative comparisons with manually obtained results as well as with results obtained by a two-dimensional segmentation method are used to illustrate how the additional information provided by three-dimensional image stacks can improve segmentation results.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Anatomy, Cross-Sectional / methods*
  • Artificial Intelligence
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Microscopy, Confocal / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Saccharomyces cerevisiae / cytology*
  • Sensitivity and Specificity