R: Create a cross-correlated brightness time-series.

cc_brightness_timeseries {nandb}

R Documentation

Create a cross-correlated brightness time-series.

Description

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.

Usage

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 ith slice is the ith cross-correlated brightness image.

Examples


img <- ijtiff::read_tif(system.file("extdata", "two_ch.tif",
  package = "nandb"
))
cc_bts <- cc_brightness_timeseries(img, 10,
  thresh = "Huang",
  filt = "median", parallel = 2
)
ijtiff::display(cc_bts[, , 1, 1])