nandb (version 0.2.1)

MeanIntensity: Calculate mean intensity from image series.

Description

Given a time stack of images, MeanIntensity calculates the mean intensity for each pixel, returning a matrix. MeanIntensityTxtFolder does this for every image in a folder, writing the results as text files via WriteImageTxt().

Usage

MeanIntensity(arr, n.ch = 1, mst = NULL, filt = NULL,
  skip.consts = FALSE, verbose = FALSE)
MeanIntensityTxtFolder(folder.path = ".", mst = NULL, ext = "tif",
  mcc = parallel::detectCores(), verbose = TRUE)
MeanIntensities(arr3d.list, mst = NULL, skip.consts = FALSE, fail = NA,
  filt = NULL, verbose = FALSE, mcc = parallel::detectCores())

Arguments

arr

An array, can be 3- or 4-dimensional. The first two slots give the x- and y-cordinates of pixels respectively. If the array is 3-dimensional, the third slot gives the index of the frame. If it is 4-dimensional, the third slot indexes the channel and the fourth indexes the frame in the stack.. To perform this on a file that has not yet been read in, set this argument to the path to that file (a string).

n.ch

The number of channels in the image (default 1).

mst

Do you want to apply an intensity threshold prior to calculating mean intensities (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. Pixels failing to exceed the threshold are set to NA.

filt

Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the mean intensity image using SmoothFilterB() or MedianFilterB() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. If you want to smooth/median filter the mean intensity image in a different way, first calculate the mean intensities without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.

skip.consts

An image array with only one value (a 'constant array') won't threshold properly. By default the function would give an error, but by setting this parameter to TRUE, the array would instead be skipped (the function will return the original array) and give a warning.

verbose

If arr is specified as a file name, print a message to tell the user that that file is now being processed (useful for MeanIntensityTxtFolder, does not work with multiple cores) and to tell when MeanIntensityTxtFolder is done.

folder.path

The path (relative or absolute) to the folder you wish to process.

ext

The file extension of the images in the folder that you wish to process. You must wish to process all files with this extension; if there are files that you don't want to process, take them out of the folder. The default is for tiff files. Do not use regular expression in this argument.

mcc

The number of parallel cores to use for the processing.

arr3d.list

A list of 3-dimensional arrays. To perform this on files that have not yet been read in, set this argument to the path to these files (a character vector).

fail

If thresholding is done, to which value should pixels not exceeeding the threshold be set?

Value

MeanIntensity returns a matrix, the mean-intensity image. MeanIntensities returns a list of these. The result of MeanIntensityTxtFolder is csv files written to disk (in the same folder as the input images).

Examples

# NOT RUN {
library(EBImage)
library(magrittr)
img <- ReadImageData(system.file('extdata', '50.tif', package = 'nandb'))
display(normalize(img[, , 1]), method = 'raster')
mean.intensity <- MeanIntensity(img, mst = 'Huang', filt = 'median')
display(normalize(mean.intensity), method = 'r')
two.channel.img <- abind::abind(img, img, along = 4) %>% aperm(c(1, 2, 4, 3))
mint.2ch <- MeanIntensity(two.channel.img, mst = "h", filt = "med", n.ch = 2)
setwd(tempdir())
img <- ReadImageData(system.file('extdata', '50.tif', package = 'nandb'))
WriteIntImage(img, '50.tif')
WriteIntImage(img, '50again.tif')
MeanIntensityTxtFolder(mcc = 2)

img.paths <- rep(system.file('extdata', '50.tif', package = 'nandb'), 2)
mean.intensities <- MeanIntensities(img.paths, mst = 'Huang', mcc = 2)

# }