Live cell image segmentation.
A major requirement of an automated, real-time, computer vision-based cell tracking system is an efficient method for segmenting cell images. The usual segmentation algorithms proposed in the literature exhibit weak performance on live unstained cell images, which can be characterized as being of low contrast, intensity-variant, and unevenly illuminated. We propose a two-stage segmentation strategy which involves: 1) extracting an approximate region containing the cell and part of the background near the cell, and 2) segmenting the cell from the background within this region. The approach effectively reduces the influence of peripheral background intensities and texture on the extraction of a cell region. The experimental results show that this approach for segmenting cell images is both fast and robust.
Center for Intelligent Machines, McGill University, Montreal, Quebec, Canada.
K
K
Wu
D
D
Gauthier
M D
MD
Levine
D000465:Algorithms; D002465:Cell Movement / Q000502:physiology; D002468:Cell Physiological Phenomena; D002477:Cells / Q000166:cytology*; D007091:Image Processing, Computer-Assisted; D018613:Microscopy, Confocal / Q000379:methods*