afMeasure: Calculate textural features for each region of interest...
In ellispatrick/AFremover: A method to identify and remove autofluorescent signals from multi-channel images post acquisition
Description
Usage
Arguments
Value
Description
Calculate textural features for each region of interest (ROI). Measures size, pixel correlation, standard deviation, and kurtosis of ROIs across two channels, output as a dataframe.
Usage
1
afMeasure(im1, im2, mask, imBit = 16)
Arguments
im1
A matrix containing the intensities of image 1.
im2
A matrix containing the intensities of image 2.
mask
A matrix of the mask with labelled ROIs.
imBit
bit depth of im1 and im2.
Value
A dataframe of relevant measurements for clustering.
ellispatrick/AFremover documentation built on June 11, 2019, 7:49 p.m.
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Description Usage Arguments Value
Description
Calculate textural features for each region of interest (ROI). Measures size, pixel correlation, standard deviation, and kurtosis of ROIs across two channels, output as a dataframe.
Usage
1 | afMeasure(im1, im2, mask, imBit = 16)
|
Arguments
im1 |
A matrix containing the intensities of image 1. |
im2 |
A matrix containing the intensities of image 2. |
mask |
A matrix of the mask with labelled ROIs. |
imBit |
bit depth of im1 and im2. |
Value
A dataframe of relevant measurements for clustering.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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