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.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  1. https://logosbio.com/
  2. J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
    [Crossref]
  3. A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
    [Crossref]
  4. M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
    [Crossref]
  5. D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
    [Crossref]
  6. E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
    [Crossref]
  7. M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
    [Crossref]
  8. J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Proc.8(2), 30–48 (2014).
  9. B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
    [Crossref]
  10. W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
    [Crossref]
  11. D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
    [Crossref]
  12. https://imagej.nih.gov/ij/
  13. A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
    [Crossref]
  14. http://www.cs.tut.fi/sgn/csb/simcep/tool.html .
  15. R. Gonzalez and R. Woods, “Image enhancement in the spatial domain,” in Digital Image Processing3rd ed., (Prentice Hall, 2008), chap. 4.
  16. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13(1), 146–168 (2004).
    [Crossref]
  17. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979).
    [Crossref]
  18. J. Bernsen, “Dynamic thresholding of gray-level images,” Proc. 8th Int. Conf. on Pattern Recognition, Paris, 1251–1255 (1986).
  19. J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Process. 8(2), 30–48 (2014).
  20. A. G. Shanbhag, “Utilization of information measure as a means of image thresholding,” Graph. Models Image Process 56(5), 414–419 (1994).
    [Crossref]
  21. L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

2019 (1)

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

2018 (1)

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

2017 (1)

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

2016 (1)

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

2015 (1)

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

2014 (1)

J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Process. 8(2), 30–48 (2014).

2012 (1)

M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
[Crossref]

2011 (1)

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

2009 (2)

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

2007 (1)

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

2004 (1)

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13(1), 146–168 (2004).
[Crossref]

1994 (1)

A. G. Shanbhag, “Utilization of information measure as a means of image thresholding,” Graph. Models Image Process 56(5), 414–419 (1994).
[Crossref]

1979 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979).
[Crossref]

Bernal, J.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Bernsen, J.

J. Bernsen, “Dynamic thresholding of gray-level images,” Proc. 8th Int. Conf. on Pattern Recognition, Paris, 1251–1255 (1986).

Brady, M. C.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Brancati, N.

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

Brocher, J.

J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Process. 8(2), 30–48 (2014).

J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Proc.8(2), 30–48 (2014).

Brzychczy-Wøoch, M.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Burkoreshtliev, N. V.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Cibej, U.

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

Cincotti, G.

L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

Dima, A. A.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Eichler, T.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Elliott, J. T.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Fedorov, K. D.

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Filliben, J. J.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Fischer, D.

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Forero, M. G.

M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
[Crossref]

Frucci, M.

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

Furke, S.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Gelasca, E.

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Gerdes, H.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Gonzalez, R.

R. Gonzalez and R. Woods, “Image enhancement in the spatial domain,” in Digital Image Processing3rd ed., (Prentice Hall, 2008), chap. 4.

Gragnaniello, D.

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

Halter, M.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Hidalgo, A.

M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
[Crossref]

Hodneland, D.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Huttunen, H.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

Karlaš, D.

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

Kato, K.

M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
[Crossref]

Klinger-Strobel, M.

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Kociolek, M.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Kvilekval, B.

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Lehmussola, A.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

Lojk, J.

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

Lucidi, M.

L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

Lundervold, A.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Makarewicz, O.

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Manjunath,

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Misztal, K.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Nichele, L.

L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

Noble, J. A.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

Obara, B.

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Ochonska, D.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Otsu, N.

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979).
[Crossref]

Pavlin, M.

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

Persichetti, V.

L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

Peskin, A.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Plant, A. L.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Pletz, M. W.

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Plichta, A.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Riccio, D.

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

Ruusuvuori, P.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

Šajn, L.

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

Sankur, B.

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13(1), 146–168 (2004).
[Crossref]

Selinummi, J.

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

Sezgin, M.

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13(1), 146–168 (2004).
[Crossref]

Shanbhag, A. G.

A. G. Shanbhag, “Utilization of information measure as a means of image thresholding,” Graph. Models Image Process 56(5), 414–419 (1994).
[Crossref]

Spurek, P.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Suesse, H.

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Tai, X.

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

Tang, H. C.

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Woods, R.

R. Gonzalez and R. Woods, “Image enhancement in the spatial domain,” in Digital Image Processing3rd ed., (Prentice Hall, 2008), chap. 4.

Xie, W.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

Zielinski, B.

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

Zisserman, A.

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

BMC Bioinformatics (1)

E. Gelasca, B. Obara, K. D. Fedorov, B. Kvilekval, and Manjunath, “Biosegmentation benchmark for evaluation of bioimage analysis methods,” BMC Bioinformatics 10(1), 368 (2009).
[Crossref]

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization (1)

W. Xie, J. A. Noble, and A. Zisserman, “Microscopy cell counting and detection with fully convolutional regression networks,” Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 6(3), 283–292 (2018).
[Crossref]

Cytometry, Part A (1)

A. A. Dima, J. T. Elliott, J. J. Filliben, M. Halter, A. Peskin, J. Bernal, M. Kociolek, M. C. Brady, H. C. Tang, and A. L. Plant, “Comparison of segmentation algorithms for fluorescence microscopy images of cells,” Cytometry, Part A 79A(7), 545–559 (2011).
[Crossref]

Graph. Models Image Process (1)

A. G. Shanbhag, “Utilization of information measure as a means of image thresholding,” Graph. Models Image Process 56(5), 414–419 (1994).
[Crossref]

IEEE J. Biomed. Health Inform. (1)

D. Riccio, N. Brancati, M. Frucci, and D. Gragnaniello, “A new unsupervised approach for segmenting and counting cells in high-throughput microscopy image sets,” IEEE J. Biomed. Health Inform. 23(1), 437–448 (2019).
[Crossref]

IEEE Trans. Med. Imag. (1)

D. Hodneland, N. V. Burkoreshtliev, T. Eichler, X. Tai, S. Furke, A. Lundervold, and H. Gerdes, “A unified framework for automated 3-D segmentation of surface-stained living cells and a comprehensive segmentation evaluation,” IEEE Trans. Med. Imag. 28(5), 720–738 (2009).
[Crossref]

IEEE Trans. Med. Imaging (1)

A. Lehmussola, P. Ruusuvuori, J. Selinummi, and H. Huttunen, “Computational framework for simulating fluorescence microscope images with cell populations,” IEEE Trans. Med. Imaging 26(7), 1010–1016 (2007).
[Crossref]

IEEE Trans. Sys., Man., Cyber. (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Sys., Man., Cyber. 9(1), 62–66 (1979).
[Crossref]

Int. J. Image Process. (1)

J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Process. 8(2), 30–48 (2014).

J. Electron. Imaging (1)

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13(1), 146–168 (2004).
[Crossref]

J. Microsc. (2)

M. G. Forero, K. Kato, and A. Hidalgo, “Automatic cell counting in vivo in the larval nervous system of Drosophila,” J. Microsc. 246(2), 202–212 (2012).
[Crossref]

J. Lojk, U. Čibej, D. Karlaš, L. Šajn, and M. Pavlin, “Comparison of two automatic cell-counting solutions for fluorescent microscopic images,” J. Microsc. 260(1), 107–116 (2015).
[Crossref]

PLoS One (2)

B. Zieliński, A. Plichta, K. Misztal, P. Spurek, M. Brzychczy-Wøoch, and D. Ochońska, “Deep learning approach to bacterial colony classification,” PLoS One 12(9), e0184554 (2017).
[Crossref]

M. Klinger-Strobel, H. Suesse, D. Fischer, M. W. Pletz, and O. Makarewicz, “A novel computerized cell count algorithm for biofilm analysis,” PLoS One 11(5), e0154937 (2016).
[Crossref]

Other (7)

J. Brocher, “Qualitative and quantitative evaluation of two new histogram limiting binarization algorithms,” Int. J. Image Proc.8(2), 30–48 (2014).

https://logosbio.com/

J. Bernsen, “Dynamic thresholding of gray-level images,” Proc. 8th Int. Conf. on Pattern Recognition, Paris, 1251–1255 (1986).

http://www.cs.tut.fi/sgn/csb/simcep/tool.html .

R. Gonzalez and R. Woods, “Image enhancement in the spatial domain,” in Digital Image Processing3rd ed., (Prentice Hall, 2008), chap. 4.

https://imagej.nih.gov/ij/

L. Nichele, V. Persichetti, M. Lucidi, and G. Cincotti, “Thresholding algorithms for microbial cell counting,” Proc. International Conference on Optical Transparent Networks (ICTON) (2019).

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Figures (11)

Fig. 1.
Fig. 1. Starting from the left-hand side: experimental, simulated and ground-truth images for S.aureus (first row) and E. coli (second row).
Fig. 2.
Fig. 2. Image processing pipeline. On the right-hand side the ImageJ commands are reported.
Fig. 3.
Fig. 3. Mean and standard deviation of the Relative Quality parameter of global thresholding algorithms applied to synthetic images.
Fig. 4.
Fig. 4. Mean and standard deviation of the Relative Quality parameter of global thresholding algorithms applied to experimental confocal images.
Fig. 5.
Fig. 5. ROC space representation of global thresholding algorithms applied to synthetic images.
Fig. 6.
Fig. 6. ROC space representation of global thresholding algorithms applied to confocal images
Fig. 7.
Fig. 7. Mean and standard deviation of the Relative Quality parameter of local thresholding algorithms applied to synthetic images.
Fig. 8.
Fig. 8. Mean and standard deviation of the Relative Quality parameter of local thresholding algorithms applied to experimental confocal images.
Fig. 9.
Fig. 9. Relative Quality parameter of local Bernsen (with different ROI radii) and global Otsu thresholding algorithms applied to synthetic images of (a) 350 and (b) and 700 cells. Blurring effect using a Gaussian filtering with different values of standard deviation has been included.
Fig. 10.
Fig. 10. Relative Quality parameter of local Bernsen (with different ROI radii) and global Otsu thresholding algorithms applied to synthetic images of 350 cells. Additive Gaussian noise has been applied with different SNR values. (a) without pre-processing and (b) using pre-processing.
Fig. 11.
Fig. 11. Average deviation of the Area, Perimeter and Circularity parameters of synthetic images, with respect to the digital phantoms.

Tables (2)

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Table 1. Parameters of the Synthetic Images

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Table 2. Number of Cells

Equations (3)

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Relative Quality % = 100 T P T P + F P + F N
Sensitivity =  T P T P + F N
Specificity =  T N T N + F P .

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