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 colt 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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Nichele, L., Persichetti, V., Lucidi, M., Cincotti, G. (2020). Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting. OSA CONTINUUM, 3(6), 1417-1427 [10.1364/osac.393971].
Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting
Lorenzo Nichele;Massimiliano Lucidi;Gabriella Cincotti
2020-01-01
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 colt 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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing AgreementI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
