Title :
A K-Means Segmentation Method for Finding 2-D Object Areas Based on 3-D Image Stacks Obtained by Confocal Microscopy
Author :
Niemisto, A. ; Korpelainen, T. ; Saleem, R. ; Yli-Harja, O. ; Aitchison, J. ; Shmulevich, I.
Author_Institution :
Inst. for Syst. Biol., Seattle
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.
Keywords :
biological techniques; cellular biophysics; image segmentation; optical microscopy; K-means clustering; K-means segmentation method; confocal microscopy; global thresholding; mathematical morphology; three-dimensional image stacks; two-dimensional object areas; yeast; Biomedical signal processing; Fungi; Image analysis; Image segmentation; Microscopy; Morphology; Pixel; Sugar; Systems biology; USA Councils; Image analysis; K-means clustering; confocal microscope; image stack; mathematical morphology; segmentation; three-dimensional; thresholding; yeast; Algorithms; Anatomy, Cross-Sectional; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Microscopy, Confocal; Pattern Recognition, Automated; Reproducibility of Results; Saccharomyces cerevisiae; Sensitivity and Specificity;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4353606
