Create a cross-correlated brightness time-series.

Given a stack of images img, use the first frames_per_set of them to create one cross-correlated brightness image, the next frames_per_set of them to create the next and so on to get a time-series of cross-correlated brightness images.

cc_brightness_timeseries(
  img,
  frames_per_set,
  overlap = FALSE,
  ch1 = 1,
  ch2 = 2,
  thresh = NULL,
  detrend = FALSE,
  quick = FALSE,
  filt = NULL,
  parallel = FALSE
)

Arguments

img	A 4-dimensional array of images indexed by img[y, x, channel, frame] (an object of class ijtiff::ijtiff_img). The image to perform the calculation on. To perform this on a file that has not yet been read in, set this argument to the path to that file (a string).
frames_per_set	The number of frames with which to calculate the successive cross-correlated brightnesses. This may discard some images, for example if 175 frames are in the input and frames_per_set = 50, then the last 25 are discarded. If bleaching or/and thresholding are selected, they are performed on the whole image stack before the sectioning is done for calculation of cross-correlated brightnesses.
overlap	A boolean. If TRUE, the windows used to calculate brightness are overlapped, if FALSE, they are not. For example, for a 20-frame image series with 5 frames per set, if the windows are not overlapped, then the frame sets used are 1-5, 6-10, 11-15 and 16-20; whereas if they are overlapped, the frame sets are 1-5, 2-6, 3-7, 4-8 and so on up to 16-20.
ch1	A natural number. The index of the first channel to use.
ch2	A natural number. The index of the second channel to use.
thresh	Do you want to apply an intensity threshold prior to calculating cross-correlated brightness (via autothresholdr::mean_stack_thresh())? If so, set your thresholding method here. If this is a single value, that same threshold will be applied to both channels. If this is a length-2 vector or list, then these two thresholds will be applied to channels 1 and 2 respectively. A value of NA for either channel gives no thresholding for that channel.
detrend	Detrend your data with detrendr::img_detrend_rh(). This is the best known detrending method for brightness analysis. For more fine-grained control over your detrending, use the detrendr package. To detrend one channel and not the other, specify this as a length 2 vector.
quick	FALSE repeats the detrending procedure (which has some inherent randomness) a few times to hone in on the best detrend. TRUE is quicker, performing the routine only once. FALSE is better.
filt	Do you want to smooth (filt = 'smooth') or median (filt = 'median') filter the cross-correlated brightness image using smooth_filter() or median_filter() respectively? If selected, these are invoked here with a filter radius of 1 and with the option na_count = TRUE. A value of NA for either channel gives no thresholding for that channel. If you want to smooth/median filter the cross-correlated brightness image in a different way, first calculate the cross-correlated brightnesses without filtering (filt = NULL) using this function and then perform your desired filtering routine on the result.
parallel	Would you like to use multiple cores to speed up this function? If so, set the number of cores here, or to use all available cores, use parallel = TRUE.

Value

An array where the \(i\)th slice is the \(i\)th cross-correlated brightness image.

See also

brightness().

Examples

# \donttest{
img <- ijtiff::read_tif(system.file("extdata", "two_ch.tif",
  package = "nandb"
))
#> Reading two_ch.tif: an 8-bit, 30x28 pixel image of unsigned
#> integer type. Reading 2 channels and 100 frames . . .
#>  Done.
cc_bts <- cc_brightness_timeseries(img, 10,
  thresh = "Huang",
  filt = "median", parallel = 2
)
ijtiff::display(cc_bts[, , 1, 1])
#> Using basic display functionality.
#>   * For better display functionality, install the EBImage package.
#>   * To install `EBImage`:
#>     - Install `BiocManager` with `install.packages("BiocManager")`.
#>     - Then run `BiocManager::install("EBImage")`.
# }