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- W4313482937 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." @default.
- W4313482937 created "2023-01-06" @default.
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- W4313482937 date "2023-01-30" @default.
- W4313482937 modified "2023-10-16" @default.
- W4313482937 title "Robust and unbiased estimation of the background distribution for automated quantitative imaging" @default.
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- W4313482937 doi "https://doi.org/10.1364/josaa.477468" @default.
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