Help for package autothresholdr

Type:

Package

Title:

An R Port of the 'ImageJ' Plugin 'Auto Threshold'

Version:

1.4.2

Maintainer:

Rory Nolan <rorynoolan@gmail.com>

Description:

Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See https://imagej.net/plugins/auto-threshold and Landini et al. (2017) <doi:10.1111/jmi.12474>.

License:

GPL-3

URL:

https://rorynolan.github.io/autothresholdr/, https://github.com/rorynolan/autothresholdr#readme

BugReports:

https://github.com/rorynolan/autothresholdr/issues

Depends:

R (≥ 3.5)

Imports:

checkmate (≥ 1.9.3), ijtiff (≥ 2.2), magrittr (≥ 1.5), purrr, Rcpp (≥ 1.0.11), rlang (≥ 0.3.3), stats, strex (≥ 1.4.1), stringr (≥ 1.4)

Suggests:

covr, dplyr, ggplot2, knitr, rmarkdown, spelling, styler (≥ 1.3.2), testthat (≥ 3.0), utils

LinkingTo:

Rcpp (≥ 1.0.11)

VignetteBuilder:

knitr

Config/testthat/edition:

Encoding:

UTF-8

Language:

en-US

RoxygenNote:

7.2.3

NeedsCompilation:

yes

Packaged:

2023-12-13 05:28:35 UTC; rorynolan

Author:

Rory Nolan

[aut, cre, trl], Luis Alvarez

[ctb], Sergi Padilla-Parra

[ctb, ths], Gabriel Landini

[ctb, cph]

Repository:

CRAN

Date/Publication:

2023-12-13 06:00:02 UTC

Automatically threshold an array of non-negative integers.

Description

These functions apply the ImageJ "Auto Threshold" plugin's image thresholding methods. The available methods are "IJDefault", "Huang", "Huang2", "Intermodes", "IsoData", "Li", "MaxEntropy", "Mean", "MinErrorI", "Minimum", "Moments", "Otsu", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle" and "Yen". Read about them at https://imagej.net/plugins/auto-threshold.

Usage

auto_thresh(
  int_arr,
  method,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

auto_thresh_mask(
  int_arr,
  method,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

auto_thresh_apply_mask(
  int_arr,
  method,
  fail = NA,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

mask(
  int_arr,
  method,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

apply_mask(
  int_arr,
  method,
  fail = NA,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

Arguments

int_arr

An array (or vector) of non-negative integers.

method

The name of the thresholding method you wish to use. The available methods are "IJDefault", "Huang", "Huang2", "Intermodes", "IsoData", "Li", "MaxEntropy", "Mean", "MinErrorI", "Minimum", "Moments", "Otsu", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle" and "Yen". Partial matching is performed i.e. method = "h" is enough to get you "Huang" and method = "in" is enough to get you "Intermodes". To perform manual thresholding (where you set the threshold yourself), supply the threshold here as a number e.g. method = 3; so note that this would not select the third method in the above list of methods.

ignore_black

Ignore black pixels/elements (zeros) when performing the thresholding?

ignore_white

Ignore white pixels when performing the thresholding? If set to TRUE, the function makes a good guess as to what the white (saturated) value would be (see 'Details'). If this is set to a number, all pixels with value greater than or equal to that number are ignored.

ignore_na

This should be TRUE if NAs in int_arr should be ignored or FALSE if you want the presence of NAs in int_arr to throw an error.

fail

When using auto_thresh_apply_mask(), to what value do you wish to set the pixels which fail to exceed the threshold? fail = 'saturate' sets them to saturated value (see "Details"). fail = 'zero' sets them to zero. You can also specify directly here a natural number (must be between 0 and 2^16 - 1) to use.

Details

Values greater than or equal to the found threshold pass the thresholding and values less than the threshold fail the thresholding.
For ignore_white = TRUE, if the maximum value in the array is one of 2^8-1, 2^12-1, 2^16-1 or 2^32-1, then those max values are ignored. That's because they're the white values in 8, 12, 16 and 32-bit images respectively (and these are the common image bit sizes to work with). This guesswork has to be done because R does not know how many bits the image was on disk. This guess is very unlikely to be wrong, and if it is, the consequences are negligible anyway. If you're very concerned, then just specify the white value as an integer in this ignore_white argument.
If you have set ignore_black = TRUE and/or ignore_white = TRUE but you are still getting error/warning messages telling you to try them, then your chosen method is not working for the given array, so you should try a different method.
For a given array, if all values are less than 2^8, saturated value is 2^8 - 1, otherwise, if all values are less than 2^16, the saturated value is 2^16 - 1, otherwise the saturated value is 2^32-1.
For the auto_thresh() function, if you pass int_arr as a data frame with column names value and n, that's the same as passing an integer array having n entries of each value. For this form of int_arr, ignore_white and ignore_black are irrelevant.

Value

auto_thresh() returns an object of class th containing the threshold value. Pixels exceeding this threshold pass the thresholding, pixels at or below this level fail.

auto_thresh_mask() returns an object of class masked_arr which is a binarized version of the input, with a value of TRUE at points which exceed the threshold and FALSE at those which do not.

auto_thresh_apply_mask() returns and object of class threshed_arr which is the original input masked by the threshold, i.e. all points not exceeding the threshold are set to a user-defined value (default NA).

mask() is the same as auto_thresh_mask() and apply_mask() is the same as auto_thresh_apply_mask().

Acknowledgements

Gabriel Landini coded all of these functions in Java. These java functions were then translated to C++.

References

Huang, L-K & Wang, M-J J (1995), "Image thresholding by minimizing the measure of fuzziness", Pattern Recognition 28(1): 41-51
Prewitt, JMS & Mendelsohn, ML (1966), "The analysis of cell images", Annals of the New York Academy of Sciences 128: 1035-1053
Ridler, TW & Calvard, S (1978), "Picture thresholding using an iterative selection method", IEEE Transactions on Systems, Man and Cybernetics 8: 630-632
Li, CH & Lee, CK (1993), "Minimum Cross Entropy Thresholding", Pattern Recognition 26(4): 617-625
Li, CH & Tam, PKS (1998), "An Iterative Algorithm for Minimum Cross Entropy Thresholding", Pattern Recognition Letters 18(8): 771-776
Sezgin, M & Sankur, B (2004), "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation", Journal of Electronic Imaging 13(1): 146-165
Kapur, JN; Sahoo, PK & Wong, ACK (1985), "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram", Graphical Models and Image Processing 29(3): 273-285
Glasbey, CA (1993), "An analysis of histogram-based thresholding algorithms", CVGIP: Graphical Models and Image Processing 55: 532-537
Kittler, J & Illingworth, J (1986), "Minimum error thresholding", Pattern Recognition 19: 41-47
Prewitt, JMS & Mendelsohn, ML (1966), "The analysis of cell images", Annals of the New York Academy of Sciences 128: 1035-1053
Tsai, W (1985), "Moment-preserving thresholding: a new approach", Computer Vision, Graphics, and Image Processing 29: 377-393
Otsu, N (1979), "A threshold selection method from gray-level histograms", IEEE Trans. Sys., Man., Cyber. 9: 62-66, doi:10.1109/TSMC.1979.4310076
Doyle, W (1962), "Operation useful for similarity-invariant pattern recognition", Journal of the Association for Computing Machinery 9: 259-267, doi:10.1145/321119.321123
Kapur, JN; Sahoo, PK & Wong, ACK (1985), "A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram", Graphical Models and Image Processing 29(3): 273-285
Shanbhag, Abhijit G. (1994), "Utilization of information measure as a means of image thresholding", Graph. Models Image Process. (Academic Press, Inc.) 56 (5): 414–419, ISSN 1049-9652
Zack GW, Rogers WE, Latt SA (1977), "Automatic measurement of sister chromatid exchange frequency", J. Histochem. Cytochem. 25 (7): 74153, PMID 70454
Yen JC, Chang FJ, Chang S (1995), "A New Criterion for Automatic Multilevel Thresholding", IEEE Trans. on Image Processing 4 (3): 370-378, ISSN 1057-7149, doi:10.1109/83.366472
Sezgin, M & Sankur, B (2004), "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation", Journal of Electronic Imaging 13(1): 146-165

Examples


img_location <- system.file("extdata", "eg.tif", package = "autothresholdr")
img <- ijtiff::read_tif(img_location)
auto_thresh(img, "huang")
img_value_count <- magrittr::set_names(as.data.frame(table(img)),
                                      c("value", "n"))
print(head(img_value_count))
auto_thresh(img_value_count, "Huang")
auto_thresh(img, "tri")
auto_thresh(img, "Otsu")
auto_thresh(img, 9)
mask <- auto_thresh_mask(img, "huang")
ijtiff::display(mask[, , 1, 1])
masked <- auto_thresh_apply_mask(img, "huang")
ijtiff::display(masked[, , 1, 1])
masked <- auto_thresh_apply_mask(img, 25)
ijtiff::display(masked[, , 1, 1])

Masked array class.

Description

A mask of an array with respect to a given threshold is found by taking the original array and setting all elements falling below the threshold to FALSE and the others to TRUE. An object of class masked_arr has the attribute thresh detailing the threshold value that was applied.

Usage

masked_arr(arr, thresh)

Arguments

arr

An array of logicals (the mask).

thresh

The threshold. Either a scalar or an object of class th.

Value

An object of class masked_arr.

Threshold every image frame in an image stack based on their mean.

Description

An ijtiff_img is a 4-dimensional array indexed by img[y, x, channel, frame]. For each channel (which consists of a stack of frames), this function finds a threshold based on the sum all of the frames, uses this to create a mask and then applies this mask to every frame in the stack (so for a given pillar in the image stack, either all the pixels therein are thresholded away or all are untouched, where pillar ⁠x,y⁠ of channel ch is img[y, x, ch, ]).

Usage

mean_stack_thresh(
  img,
  method,
  fail = NA,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

Arguments

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]).

method

The name of the thresholding method you wish to use. The available methods are "IJDefault", "Huang", "Huang2", "Intermodes", "IsoData", "Li", "MaxEntropy", "Mean", "MinErrorI", "Minimum", "Moments", "Otsu", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle" and "Yen". Partial matching is performed i.e. method = "h" is enough to get you "Huang" and method = "in" is enough to get you "Intermodes". To perform manual thresholding (where you set the threshold yourself), supply the threshold here as a number e.g. method = 3.8 (so note that this would not select the third method in the above list of methods). This manual threshold will then be used to threshold the sum stack to create a 2D mask and then this mask will be applied to all frames in the stack. If you want a different method for each channel, specify this parameter as a vector or list, one element per channel.

fail

When using auto_thresh_apply_mask(), to what value do you wish to set the pixels which fail to exceed the threshold? fail = 'saturate' sets them to saturated value (see 'Details'). fail = 'zero' sets them to zero. You can also specify directly here a natural number (must be between 0 and 2^16 - 1) to use.

ignore_black

Ignore black pixels/elements (zeros) when performing the thresholding?

ignore_white

ignore_na

This should be TRUE if NAs in int_arr should be ignored or FALSE if you want the presence of NAs in int_arr to throw an error.

Details

It's called mean_stack_thresh() and not sum_stack_thresh() because its easier for people to visualize the mean of an image series than to visualize the sum, but for the sake of this procedure, both are equivalent, except for the fact that the thresholding routine invoked inside this function prefers integers, which we get by using a sum but not by using a mean.

Values greater than or equal to the found threshold pass the thresholding and values less than the threshold fail the thresholding.
For ignore_white = TRUE, if the maximum value in the array is one of 2^8-1, 2^16-1 or 2^32-1, then those max values are ignored. That's because they're the white values in 8, 16 and 32-bit images respectively (and these are the common image bit sizes to work with). This guesswork has to be done because R does not know how many bits the image was on disk. This guess is very unlikely to be wrong, and if it is, the consequences are negligible anyway. If you're very concerned, then just specify the white value as an integer in this ignore_white argument.
If you have set ignore_black = TRUE and/or ignore_white = TRUE but you are still getting error/warning messages telling you to try them, then your chosen method is not working for the given array, so you should try a different method.
For a given array, if all values are less than 2^8, saturated value is 2^8 - 1, otherwise, saturated value is 2^16 - 1.

Value

An object of class stack_threshed_img which is the thresholded image (an array in the style of an ijtiff_img). Pillars not exceeding the threshold are set to the fail value (default NA).

Examples


img <- ijtiff::read_tif(system.file("extdata", "50.tif",
  package = "autothresholdr"
))
ijtiff::display(img[, , 1, 1])
img_thresh_mask <- mean_stack_thresh(img, "Otsu")
ijtiff::display(img_thresh_mask[, , 1, 1])
ijtiff::display(img[, , 1, 1])
img_thresh_mask <- mean_stack_thresh(img, "Huang")
ijtiff::display(img_thresh_mask[, , 1, 1])

Threshold every image frame in a stack based on their median.

Description

An ijtiff_img is a 4-dimensional array indexed by img[y, x, channel, frame]. For each channel (which consists of a stack of frames), this function finds a threshold based on all of the frames, then takes the median of all the frames in the stack image, uses this to create a mask with the found threshold and then applies this mask to every frame in the stack (so for a given pillar in the image stack, either all the pixels therein are thresholded away or all are untouched, where pillar ⁠x,y⁠ of channel ch is img[y, x, ch, ]).

Usage

med_stack_thresh(
  img,
  method,
  fail = NA,
  ignore_black = FALSE,
  ignore_white = FALSE,
  ignore_na = FALSE
)

Arguments

img

A 3-dimensional array (the image stack, possibly a time-series of images) where the nth slice is the nth image in the stack.

method

The name of the thresholding method you wish to use. The available methods are "IJDefault", "Huang", "Huang2", "Intermodes", "IsoData", "Li", "MaxEntropy", "Mean", "MinErrorI", "Minimum", "Moments", "Otsu", "Percentile", "RenyiEntropy", "Shanbhag", "Triangle" and "Yen". Partial matching is performed i.e. method = "h" is enough to get you "Huang" and method = "in" is enough to get you "Intermodes". To perform manual thresholding (where you set the threshold yourself), supply the threshold here as a number e.g. method = 3 (so note that this would not select the third method in the above list of methods). This manual threshold will then be used to threshold the median stack to create a 2D mask and then this mask will be applied to all frames in the stack. If you want a different method for each channel, specify this parameter as a vector or list, one element per channel.

fail

ignore_black

Ignore black pixels/elements (zeros) when performing the thresholding?

ignore_white

Ignore white pixels when performing the thresholding? If set to TRUE, the function makes a good guess as to what the white (saturated) value would be (see 'Details').

ignore_na

This should be TRUE if NAs in int_arr should be ignored or FALSE if you want the presence of NAs in int_arr to throw an error.

Details

Values greater than or equal to the found threshold pass the thresholding and values less than the threshold fail the thresholding.
For ignore_white = TRUE, if the maximum value in the array is one of 2^8-1, 2^16-1 or 2^32-1, then those max values are ignored. That's because they're the white values in 8, 16 and 32-bit images respectively (and these are the common image bit sizes to work with). This guesswork has to be done because R does not know how many bits the image was on disk. This guess is very unlikely to be wrong, and if it is, the consequences are negligible anyway. If you're very concerned, then just specify the white value as an integer in this ignore_white argument.
If you have set ignore_black = TRUE and/or ignore_white = TRUE but you are still getting error/warning messages telling you to try them, then your chosen method is not working for the given array, so you should try a different method.
For a given array, if all values are less than 2^8, saturated value is 2^8 - 1, otherwise, saturated value is 2^16 - 1.

Value

An object of class stack_threshed_img which is the thresholded image (an array in the style of an ijtiff_img). Pillars not exceeding the threshold are set to the fail value (default NA).

Examples


img <- ijtiff::read_tif(system.file("extdata", "50.tif",
  package = "autothresholdr"
))
ijtiff::display(img[, , 1, 1])
img_thresh_mask <- med_stack_thresh(img, "Otsu")
ijtiff::display(img_thresh_mask[, , 1, 1])
ijtiff::display(img[, , 1, 1])
img_thresh_mask <- med_stack_thresh(img, "Triangle")
ijtiff::display(img_thresh_mask[, , 1, 1])

Stack-thresholded image class.

Description

A stack-thresholded array is an array which has had stack-thresholding applied to it. See mean_stack_thresh(). It has 3 necessary attributes:

thresh is the threshold that was applied. This is either a number or an object of class th. Values in the original array which were less than this value are deemed to have failed the thresholding.
fail_value is the value to which elements of the array which failed the thresholding were set. This could be something like 0 or NA.
stack_thresh_method details which stacked-thresholding method was employed; this is either "mean" or "median".

Usage

stack_threshed_img(img, thresh, fail_value, stack_thresh_method)

Arguments

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]).

thresh

The threshold that was used. Either a number or an object of class th.

fail_value

The value to which elements of the array which failed the thresholding were set.

stack_thresh_method

This must be set to either "mean" or "median" to tell which stacked-thresholding method was employed.

Value

An object of class stack_threshed_img.

Automatically found threshold class.

Description

A threshold found automatically via auto_thresh(). It is a number (the value of the threshold) with 4 attributes:

ignore_black is TRUE if black values were ignored during the thresholding and FALSE otherwise.
ignore_white is TRUE if white values were ignored during the thresholding and FALSE otherwise.
ignore_na is TRUE if NAs were ignored during the thresholding and FALSE otherwise.
autothresh_method details which automatic thresholding method was used.

Usage

th(thresh, ignore_black, ignore_white, ignore_na, autothresh_method)

Arguments

thresh

A scalar. The threshold.

ignore_black

TRUE if black values were ignored during the thresholding and FALSE otherwise.

ignore_white

TRUE if white values were ignored during the thresholding and FALSE otherwise.

ignore_na

TRUE if NA values were ignored during the thresholding and FALSE otherwise.

autothresh_method

The name of the automatic thresholding method used.

Value

An object of class th.

Thresholded array class.

Description

A thresholded array is an array which has had a threshold applied to it. It has an attribute thresh which is the threshold that was applied which can be a number or an object of class th.

Usage

threshed_arr(arr, thresh)

Arguments

arr

The thresholded array (not the original array).

thresh

The threshold that was used. Either a number or an object of class th.

Details

The term 'array' is used loosely here in that vectors and matrices qualify as arrays.

Value

An object of class threshed_arr.

Automatically threshold an array of non-negative integers.

Description

Usage

Arguments

Details

Value

Acknowledgements

References

Examples

Masked array class.

Description

Usage

Arguments

Value

Threshold every image frame in an image stack based on their mean.

Description

Usage

Arguments

Details

Value

Examples

Threshold every image frame in a stack based on their median.

Description

Usage

Arguments

Details

Value

Examples

Stack-thresholded image class.

Description

Usage

Arguments

Value

See Also

Automatically found threshold class.

Description

Usage

Arguments

Value

Thresholded array class.

Description

Usage

Arguments

Details

Value

See Also