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Robust and unbiased estimation of the background distribution for automated quantitative imaging

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Abstract

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.

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Supplementary Material (1)

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Supplement 1       Supplemental document.

Data availability

The code to reproduce all figures and a Python implementation of SMO is available at [29]. We also provide plugins for napari [30], CellProfiler [31], and ImageJ/FIJI [16]. It is also published in PyPI and conda-forge. All image and figure generation was made with the Python scientific stack (NumPy [32], SciPy [33], matplotlib [34], scikit-image [22]).

29. M. Silberberg and G. Peña-Castellanos, “Robust and unbiased estimation of the background distribution for automated quantitative imaging,” GitHub (2022), https://github.com/maurosilber/SMO.

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31. D. R. Stirling, M. J. Swain-Bowden, A. M. Lucas, A. E. Carpenter, B. A. Cimini, and A. Goodman, “Cellprofiler 4: improvements in speed, utility and usability,” BMC Bioinf. 22, 433 (2021). [CrossRef]  

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32. C. R. Harris, K. J. Millman, S. J. van der Walt, et al., “Array programming with numpy,” Nature 585, 357–362 (2020). [CrossRef]  

33. P. Virtanen, R. Gommers, T. E. Oliphant, et al., “Scipy 1.0: fundamental algorithms for scientific computing in Python,” Nat. Methods 17, 261–272 (2020). [CrossRef]  

34. J. D. Hunter, “Matplotlib: a 2D graphics environment,” Comput. Sci. Eng. 9, 90–95 (2007). [CrossRef]  

22. S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014). [CrossRef]  

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