auto_thresh {autothresholdr}

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])