Auto Local Threshold
From Fiji
| Auto Local Threshold (ImageJ) | |
|---|---|
| Author | Gabriel Landini, plus others (see below) |
| Maintainer | Gabriel Landini (G.Landini at bham. ac. uk) |
| File | included in Auto_Threshold.jar |
| Source | on gitweb(1 file) |
| Latest version | v1.2 (25 May 2010) |
| Development status | active |
Contents |
Purpose
This plugin binarises 8-bit images using various local thresholding methods. By 'local' here is meant that the threshold is computed for each pixel according to the image characteristings within a window of radius r around it. The segmented phase is always shown as white (255).
For global thresholding rather than local, see the Auto Threshold plugin.
Installation
ImageJ: requires v1.42m or newer. Copy the Auto_Threshold.jar file from http://www.dentistry.bham.ac.uk/landinig/software/auto_threshold.jar into the ImageJ/Plugins folder and either restart ImageJ or run the Help>Update Menus command. After this a new command should appear in Image>Adjust>Auto Local Threshold.
Fiji: this plugin is part of the Fiji distribution, there is no need to download it.
Use
Method selects the algorithm to be applied (detailed below).
The radius sets the radius of the local domain over which the threshold will be computed.
White object on black background sets to white the pixels with values above the threshold value (otherwise, it sets to white the values less or equal to the threshold).
Special parameters 1 and 2 sets specific values for each method. Those are detailed below for each method.
It you are processing a stack, one additional option is available: Stack can be used to process all the slices.
Available methods
Try all
Which method segments your data best? You can attempt to answer this question using the Try all option. This produces a montage with results from all the methods, so one can explore how the different algorithms perform on an image or stack.
Original image
Try all methods.
When processing stacks with many slices, the montages can become very large (several times the original stack size) and one risks running out of ram. A popup window will appear (when stacks have more than 25 slices) to confirm whether the procedure should display the stack montages.
Bernsen
Implements Bernsen's thresholding method. Note that this implementation uses circular windows instead of rectangular in the original.
Parameter 1: is the contrast threshold. The default value is 15. Any number different than 0 will change the default value.
Parameter 2: not used, ignored.
The method uses a user-provided contrast threshold. The threshold is set at the midgrey value (the mean of the minimum and maximum grey values in the local window). If the local contrast (max-min) is below the contrast threshold the neighbourhood is considered to consist only of one class.
if ( local_contrast < contrast_threshold ) pixel = ( mid_gray >= 128 ) ? object : background else pixel = (pixel >= mid_gray ) ? object : background
- Bernsen, J (1986), "Dynamic Thresholding of Grey-Level Images", Proc. of the 8th Int. Conf. on Pattern Recognition
- Sezgin, M & Sankur, B (2004), "Survey over Image Thresholding Techniques and Quantitative Performance Evaluation", Journal of Electronic Imaging 13(1): 146-165, <http://citeseer.ist.psu.edu/sezgin04survey.html>
Based on ME Celebi's fourier_0.8 routines [1] and [2].
Mean
This selects the threshold as the mean of the local greyscale distribution. A variation of this method uses the mean - C, where C is a constant.
Parameter 1: is the C value. The default value is 0. Any other number will change the default value.
Parameter 2: not used, ignored.
http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
Median
This selects the threshold as the median of the local greyscale distribution. A variation of this method uses the median - C, where C is a constant.
Parameter 1: is the C value. The default value is 0. Any other number will change the default value.
Parameter 2: not used, ignored.
http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
MidGrey
This selects the threshold as the mid-grey of the local greyscale distribution (i.e. (max + min)/2. A variation of this method uses the median - C, where C is a constant.
Parameter 1: is the C value. The default value is 0. Any other number will change the default value.
Parameter 2: not used, ignored.
http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
Niblack
Implements Niblack's thresholding method:
pixel = ( pixel > mean + k * standard_deviation) ? object : background
Parameter 1: is the k value. The default value is 0.2 for bright objects and -0.2 for dark objects. Any other number than 0 will change the default value.
Parameter 2: not used, ignored.
- Niblack, W (1986), An introduction to Digital Image Processing" Prentice-Hall
Ported from ME Celebi's fourier_0.8 routines [3] and [4].
Sauvola
Implements Sauvola's thresholding method, which is a variation of Niblack's method
pixel = (pixel > mean * (1 + k *( standard_deviation / r - 1))) ? object : background
Parameter 1: is the k value. The default value is 0.5. Any other number than 0 will change the default value.
Parameter 2: is the r value. The default value is 128. Any other number than 0 will change the default value
- Sauvola, J & Pietaksinen, M (2000), "Adaptive Document Image Binarization", Pattern Recognition 33(2): 225-236, <http://www.ee.oulu.fi/mvg/publications/show_pdf.php?ID=24>
