Sentence examples for median background estimates from inspiring English sources

For mouse arrays, background correction was performed using the 'normexp' method implemented in the Bioconductor LIMMA package to adjust local median background estimates (54).

Normexp adjusts the local median background, thus avoiding problems with estimates greater than foreground values, and ensures that there are no missing or negative corrected intensities.

Experiments on real-life surveillance videos demonstrate that the proposed method obtains considerably better background estimates (both qualitatively and quantitatively) than median filtering and the recently proposed "intervals of stable intensity" method.

For instance, in[1], the median background image was used to subtract the background components.

The median background SUVmean was similar for all reconstruction algorithms (median SUVmean, 2.8; range, 2.0 to 4.1).

Background-corrected data was obtained by calculating the median foreground and subtracting the median background signal intensity for each channel.

Median background was 38.0 - 40.7.

Essentially, signals were calculated as median intensity minus median background.

Raw median spot intensity and median background intensity data were exported; median background signal intensities were subtracted from median signal intensities.

Median foreground intensities (F) and median background intensities (B) were generated to construct the data matrix.

Median foreground pixel intensities for spots were adjusted by subtracting median background pixel intensities.