Abstract
Binarization is a key process in microscopy cell counting and cytometry analysis that is performed before segmentation to identify a cell within the background. We test the performances of 16 global and 9 local ImageJ thresholding algorithms on both experimental and synthetic confocal images of Escherichia coli and Staphylococcus aureus, evaluating the misclassification errors according to standard pattern recognition parameters. Some thresholding algorithms, such as Otsu, outperform other approaches, with respect to a pixel-by-pixel analysis. Overall, we found that the Bernsen local thresholding furnishes the best results also with respect to cell counting and morphology analysis.
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