nandb (version 2.1.0)

cc_number: Cross-correlated number.

Description

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

Usage

cc_number(
  img,
  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).

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.

Value

A numeric matrix, the cross-correlated number image.

Examples

# NOT RUN {
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])
# }