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
)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).
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.
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.
A natural number. The index of the first channel to use.
A natural number. The index of the second channel to use.
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 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.
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.
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.
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.
An array where the \(i\)th slice is the \(i\)th cross-correlated brightness image.
# NOT RUN {
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])
# }
Run the code above in your browser using DataLab