cc_number: Cross-correlated number.
In nandb: Number and Brightness Image Analysis

Description Usage Arguments Value Examples

Given a time stack of images and two channels, calculate the cross-correlated number of those two channels for each pixel.

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

`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).
`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 number (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 number 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 number image in a different way, first calculate the cross-correlated numbers 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`.

A numeric matrix, the cross-correlated number image.

img <- ijtiff::read_tif(system.file("extdata", "two_ch.tif",
  package = "nandb"
))
ijtiff::display(detrendr::mean_pillars(img[, , 1, ]))
ijtiff::display(detrendr::mean_pillars(img[, , 2, ]))
n <- number(img, def = "n", thresh = "Huang", filt = "median")
ijtiff::display(n[, , 1, 1])
ijtiff::display(n[, , 2, 1])
cc_n <- cc_number(img, thresh = "Huang")
ijtiff::display(cc_n[, , 1, 1])