brightness_timeseries: Create a brightness time-series.
In nandb: Number and Brightness Image Analysis

Description Usage Arguments Details Value See Also Examples

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

brightness_timeseries(
  img,
  def,
  frames_per_set,
  overlap = FALSE,
  thresh = NULL,
  detrend = FALSE,
  quick = FALSE,
  filt = NULL,
  s = 1,
  offset = 0,
  readout_noise = 0,
  parallel = FALSE
)

`img`	A 4-dimensional array in the style of an ijtiff_img (indexed by `img[y, x, channel, frame]`) or a 3-dimensional array which is a single channel of an ijtiff_img (indexed by `img[y, x, frame]`).
`def`	A character. Which definition of brightness do you want to use, `"B"` or `"epsilon"`?
`frames_per_set`	The number of frames with which to calculate the successive brightnesses.
`overlap`	A boolean. If `TRUE`, the windows used to calculate number 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.
`thresh`	The threshold or thresholding method (see `autothresholdr::mean_stack_thresh()`) to use on the image prior to detrending and brightness calculations.
`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. If there are many channels, this may be specified as a vector, one element for each channel.
`quick`	If `FALSE` (the default), the swap finding routine is run several times to get a consensus for the best parameter. If `TRUE`, the swap finding routine is run only once.
`filt`	Do you want to smooth (`filt = 'mean'`) or median (`filt = 'median'`) filter the number image using `smooth_filter()` or `median_filter()` respectively? If selected, these are invoked here with a filter radius of 1 (with corners included, so each median is the median of 9 elements) and with the option `na_count = TRUE`. If you want to smooth/median filter the number image in a different way, first calculate the numbers without filtering (`filt = NULL`) using this function and then perform your desired filtering routine on the result. If there are many channels, this may be specified as a vector, one element for each channel.
`s`	A positive number. The S-factor of microscope acquisition.
`offset`	Microscope acquisition parameters. See reference Dalal et al.
`readout_noise`	Microscope acquisition parameters. See reference Dalal et al.
`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`.

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 detrending is selected, it is performed on the whole image stack before the sectioning is done for calculation of numbers.

An object of class brightness_ts_img.

If img is 3-dimensional (i.e. 1-channel), a 3-dimensional array arr is returned with arr[y, x, t] being pixel (x, y) of the tth brightness image in the brightness time series.
If img is 4-dimensional (i.e. 2-channel), a 4-dimensional array arr is returned with arr[y, x, c, t] being pixel (x, y) of the cth channel of the tth brightness image in the brightness time series.

brightness().

1 2	img <- ijtiff::read_tif(system.file("extdata", "50.tif", package = "nandb")) bts <- brightness_timeseries(img, "e", frames_per_set = 20, thresh = "Huang")