A K-means segmentation method for finding 2-D object areas based on 3-D image stacks obtained by confocal microscopy.
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.
Niemistö
Antti
A
Institute for Systems Biology, Seattle, Washington, USA.
Korpelainen
Tomi
T
Saleem
Ramsey
R
Yli-Harja
Olli
O
Aitchison
John
J
Shmulevich
Ilya
I
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000166:cytology*; D012680:Sensitivity and Specificity