Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting

This is the authors' abstract. We don't add key points for this paper.

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.