Project Fiji
Source on GitHub
License GPLv3
Release 2.0.2
Date Sat Apr 29 22:38:51 CDT 2017
Development status Stable
Support status Active
Founders Jean-Yves Tinevez
Leads Jean-Yves Tinevez
Developers -
Debuggers Jean-Yves Tinevez
Reviewers Jean-Yves Tinevez
Support Jean-Yves Tinevez
Maintainers Jean-Yves Tinevez, Curtis Rueden
Contributors Johannes Schindelin, Jan Eglinger, Mark Hiner


Directionality Example.png

This plugin is used to infer the preferred orientation of structures present in the input image. It computes a histogram indicating the amount of structures in a given direction. Images with completely isotropic content are expected to give a flat histogram, whereas images in which there is a preferred orientation are expected to give a histogram with a peak at that orientation.

For instance, in the pine tree branch pictured above, the needle shaped leaves exist in 2 populations, one with a preferred orientation at about 45º, and another one with preferred orientation around -45º. This is well detected by the plugin, which reports two main peaks at 60º and -60º. On top of that, a minor peak can be seen around 0º, reporting the main branch orientation.

Angles are reported in their common mathematical sense. That is: 0º is the East direction, and the orientation is counterclockwise.

The user interface allows to specify:

  • The number of bins (default: 90), that will partition the 180º.
  • The starting angle (default:-90º) for analysis. Angles will run from this value to 180º plus the starting value. This option was added to avoid having peaks at +90º split at the borders of the histogram.
  • The debug flag, if set, will cause angular filters and power spectrum to be displayed.
  • The result table flag will generate a table containing all result value to be displayed. This table can be exported as a CSV file thereafter; see below.
  • The orientation map flag will cause the orientation map to be generated; see below.

Statistics generated

On top of the histogram, the plugin tries to generate statistics on the highest peak found.

  • The highest peak is fitted by a Gaussian function, taking into account the periodic nature of the histogram.
  • The 'Direction (º)' column reports the center of the gaussian.
  • The 'Dispersion (º)' column reports the standard deviation of the gaussian.
  • The 'Amount' column is the sum of the histogram from center-std to center+std, divided by the total sum of the histogram. The real histogram values are used for the summation, not the gaussian fit.
  • The 'Goodness' column reports the goodness of the fit; 1 is good, 0 is bad.

A study made on artificial images reveal that the 'Amount' value as calculated here underestimates the real proportion of structures with the preferred orientation. So for the pine image up there, one can conclude that the proportion of needle leaves oriented around +60º is at least 25% (however, the image is not completely uniform, which cripples the meaning of this amount value).

Results data export

  1. Save the Directionality Results window contents to a spreadsheet by copy pasting.
  2. If you want to save an image of the histogram, right-click on the plot, and choose save as.
  3. If you want to export the histogram data, check the Data table box in the plugin dialog window. This will generate a results table, that can be exported as a csv file with a right click, or copy pasted directly into a spreadsheet etc.

How does it work?

Different solutions have been thought about, and two of them are currently implemented:

Fourier components analysis

This method is based on Fourier spectrum analysis. For a square image, structures with a preferred orientation generate a periodic pattern at +90º orientation in the Fourier transform of the image, compared to the direction of the objects in the input image.

This plugin chops the image into square pieces, and computes their Fourier power spectra. The later are analyzed in polar coordinates, and the power is measured for each angle using the spatial filters proposed in [1].

Local gradient orientation

This method is local. The gradient of the image is calculated using a 5x5 Sobel filter, and is used to derive the local gradient orientation. This orientation is then used to build the histogram, by putting the square of the gradient norm in the adequate bin. The square of the norm was retained, so as to have an histogram with the same dimension that for the Fourier analysis.

Orientation map

Since version 2.0, the plugin offers the possibility to generate an orientation map, where the image is colored according to its local directionality, or location orientation. This has a well an easily defined meaning in the case of the local gradient orientation method, but things are a bit more complicated in the case of the Fourier component, which is a global method.

In the later case, the image is filtered using the Fourier filters described above, and transformed back using inverse Fourier transform. For each pixel, the direction retained is the one that has the strongest intensity when filtered in this orientation.

To generate the orientation map image, a HSB image is made by taking

  • the local orientation as hue;
  • the original image gray value as brightness;
  • for saturation:
    • the power spectrum value for the Fourier component method;
    • the gradient magnitude square for the Local gradient orientation method.

Test images

Here is two images stack made to test the plugin accuracy:

VaryingDirection.tif is a 5 slices 16-bit stack made of artificial structures. Each slice is 512x512 pixels, and contains 2000 rods of length 100 pixels in average. 1000 of these rods have a random orientation, the others 1000 have an orientation varying from -60º to 60º from the first slice to the last one. The image is blurred by a gaussian of std 1, and corrupted by Poisson noise. You can use it to determine the accuracy of angle detection. The amount should be close and below 50%.

VaryingAmount.tif has the same parameters than the previous stack, but has a varying amount of orientated rods (from 20% to 100% of the 2000 rods) and a fixed direction of -60º for the orientated ones. You can use it to test the "Amount" score.

Code structure

This plugin is written as a classical ImageJ plugin. It implements PlugInFilter, and relies on PlugInFilterRunner to be run.

String arguments can be passed to it, using the setup(String, ImagePlus) method. For instance:

 ImagePlus imp = IJ.openImage("./TwoLines.tif");;
 Directionality_ da = new Directionality_();
 String command = "Directionality";
 new PlugInFilterRunner(da, command, "nbins=60, start=-90, method=gradient");


General scripting

It is also possible to run the plugin non-interactively from another class, or even in a script. For instance in Python:

from fiji.analyze.directionality import Directionality_
from ij import WindowManager, ImagePlus
# Instantiate plugin
dir = Directionality_()
# Set fields and settings
# dir.setMethod(fiji.analyze.directionality.Directionality_.AnalysisMethod.LOCAL_GRADIENT_ORIENTATION)
# Do calculation
# Display plot frame
plot_frame = dir.plotResults()
# Display fit analysis
data_frame = dir.displayFitAnalysis()
# Display results table
table = dir.displayResultsTable()"Directionality histograms")
# Display orientation map
stack = dir.getOrientationMap()
ImagePlus("Orientation map", stack).show()
# Generate a color wheel

Getting the fit parameters

After a request from Jessica Mansfield (University of Exeter, UK) - October 2010.

The plugin uses a fit by a gaussian to compute directionality parameters; however, the fit parameters themselves are not returned. It is possible to get them however using a script.

The gaussian fit has the following form:

y = a + (b-a)*\exp\left(-\frac{(x-c)^2}{2 d^2}\right)

The parameters are returned by the method public ArrayList<double[]> getFitParameters(), which returns a, b, c and d in an ArrayList of double[], one array per slice in the stack.

You can retrieve them and display them with the following script:

from fiji.analyze.directionality import Directionality_
from ij import WindowManager
from ij.measure import ResultsTable

# Instantiate plugin
dnlty = Directionality_()

# Set fields and settings
imp = WindowManager.getCurrentImage()
dnlty.setBuildOrientationMapFlag(False) # No orientation map

# Do calculation

# Display plot frame
plot_frame = dnlty.plotResults()

# Display fit analysis
# data_frame = dir.displayFitAnalysis()
# data_frame.setVisible(True)

# Display results table
# table = dir.displayResultsTable()
#"Directionality histograms")

# Get the fit params and put them in a results table
fitParams = dnlty. getFitParameters()
rt = ResultsTable()
index = 1;
for param in fitParams:
    rt.addValue('a', param[0])
    rt.addValue('b', param[1])
    rt.addValue('c', param[2])
    rt.addValue('d', param[3])
    index = index + 1;'Fit param for y = a + (b-a)*exp(-(x-c)*(x-c)/(2*d*d)') 

Uses of this plugin

Since there is no publication associated with this plugin, it is hard tracking where it is used. However people wrote me and helped building the following list. The Directionality plugin has been used at least in the following publications:

If you use it, please tell me so that I can add to this list.

Version history

  • v2.0: After a lot of mistakes and problems, each method now propose to generate an orientation map, which colors the image according to the local directionality.
  • v1.2: Added a new analysis method based on local gradient orientation.
  • v1.1: Added an option to export the histogram as a table.
  • v1.0: First working commit with the Fourier method.

Wish list for this plugin

Possible way for further developments:

  • Have a peak finder that can deal with multiple peaks and return statistics on them.
  • Add a radial filter to the Fourier method, that would allow to pick only structures with a given length (+/- bandwidth). Conversely, add a scale option to the Local gradient orientation method.


  • [1] Liu. Scale space approach to directional analysis of images. Appl. Opt. (1991) vol. 30 (11) pp. 1369-1373

A discussion with A. Leroy and another one with J. Schindelin are greatly acknowledged.