Automated detection and recognition of live cells in tissue culture using image cytometry

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Abstract
An automated image cytometry device, the Cell Analyzer, was used to locate live V79 cells plated at low densities in a tissue culture flask. Cells and other objects were detected by moving the flask in steps across a linear solid-state image sensor. The step size was selected to be small enough to allow detection of all the cells in the area being scanned but sufficiently large so that most cells would be detected on only one image line. To distinguish cells from other detected objects, a recognition algorithm utilizing 18 characteristic cell signal features was developed. The algorithm first tests whether a set of feature values falls within specified upper and lower bounds, and then applies a linear discriminant function to the remaining data to further discriminate cells from debris. False-positive errors of 5% or less were achieved with this method, whereas 15–35% of cells were misclassified as debris.