Thresholding Images

Although this package is for thresholding anything at all, it has added functionality for thresholding images. This vignette is all about thresholding single-frame, grayscale images. There’s another vignette about thresholding stacks of grayscale images.

library(autothresholdr)

We’ll be using the image that comes with the package:

img <- ijtiff::read_tif(system.file("extdata", "fiji_eg.tif",
  package = "autothresholdr"
))
#> Reading fiji_eg.tif: an 8-bit, 130x130 pixel image of unsigned
#> integer type. Reading 1 channel and 1 frame . . .
#> Done.
dim(img)
#> [1] 130 130   1   1
ijtiff::display(img) # displays first channel, first frame
#> Using basic display functionality.
#>   * For better display functionality, install the EBImage package.
#>   * To install `EBImage`:
#>     - Install `BiocManager` with `install.packages("BiocManager")`.
#>     - Then run `BiocManager::install("EBImage")`.

It’s a picture of cells, the black part is where the cells are not. The threshold is supposed to tell us what is dark (not cell) and what is bright (cell). By playing around, we may discover that something like 20 might (for some purposes) be a good value.

ijtiff::display(img[, , 1, 1] > 20)
#> Using basic display functionality.
#>   * For better display functionality, install the EBImage package.
#>   * To install `EBImage`:
#>     - Install `BiocManager` with `install.packages("BiocManager")`.
#>     - Then run `BiocManager::install("EBImage")`.

But what if we have many images and we don’t want to play around, we want a method of calculating the threshold automatically. https://imagej.net/plugins/auto-threshold gives many such methods and they are provided to you in R via this package. Go to that webpage for a nice comparison of the methods.

The function auto_thresh() finds the threshold, mask() gets the mask (an array with a TRUE for elements exceeding the threshold and FALSE elsewhere) and apply_mask() applies the mask to the original image by setting the elements that don’t exceed the threshold to NA.

Let’s see each with “Triangle” thresholding.

auto_thresh(img, "tri")
#> [1] 19
#> attr(,"ignore_black")
#> [1] FALSE
#> attr(,"ignore_white")
#> [1] FALSE
#> attr(,"ignore_na")
#> [1] FALSE
#> attr(,"autothresh_method")
#> [1] "Triangle"
#> attr(,"class")
#> [1] "th"      "integer"
ijtiff::display(mask(img, "tri"))
#> Using basic display functionality.
#>   * For better display functionality, install the EBImage package.
#>   * To install `EBImage`:
#>     - Install `BiocManager` with `install.packages("BiocManager")`.
#>     - Then run `BiocManager::install("EBImage")`.

ijtiff::display(apply_mask(img, "tri"))
#> Using basic display functionality.
#>   * For better display functionality, install the EBImage package.
#>   * To install `EBImage`:
#>     - Install `BiocManager` with `install.packages("BiocManager")`.
#>     - Then run `BiocManager::install("EBImage")`.

In this last image, the NA pixels are grey.

Thresholding Images

2023-12-13

Thresholding Images