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AnaMorf: Morphological Quantification of Filamentous Microbes

Dave Barry, Image Analyst, Advanced Light Microscopy, Francis Crick Institute, London, UK.


AnaMorf is a plug-in developed for the ImageJ platform ( to analyse the microscopic morphology of filamentous microbes. The program returns average data on a population of mycelial elements, using the descriptors projected area, circularity, total hyphal length, number of hyphal tips, hyphal growth unit, lacunarity and fractal dimension. The plug-in accepts as input a user-specified directory of images, analysing each and outputing tabulated results.

For the full algorithmic details of AnaMorf, please consult the following publications:

  1. Barry DJ, Chan C, Williams GA. 2009. Morphological quantification of filamentous fungal development using membrane immobilization and automatic image analysis. J Ind Microbiol Biotechnol 36:787–800.
  2. Barry DJ. 2013. Quantifying the branching frequency of virtual filamentous microbes using fractal analysis. Biotechnol Bioeng 110:437–447.


AnaMorf may be installed by downloading the latest (available through the Downloads icon on the left), unpacking the zip archive, copying all the jar files to the ImageJ plugins folder (.\ImageJ\plugins) and restarting ImageJ. The command AnaMorf > Batch Analyser should then be visible under the Plugins menu. Sample data is also included in the zip archive, together with javadocs (for developers).

User Interface

Upon selecting AnaMorf > Batch Analyser from ImageJ's Plugins menu, the user is presented with an interface, through which various parameters (described below) may be set:

BasicInterface.png AdvancedInterface.png

When the OK button is clicked, a dialog opens for the user to specify the directory in which the images to be analysed are contained. Once a valid directory is provided, the analysis proceeds and results are output to ImageJ's results table.

Basic Settings

Analysis Parameters

  • Image Format: the file extension (BMP, GIF, JPG, PNG, TIF) of the images to be analysed.
  • Image Resolution: the side length of one pixel (in microns/pixel) in the images to be analysed.
  • Minimum Branch Length: the minimum line length (in microns) required to recognize a branch as a 'true' mycelial branch, rather than an artifact of skeletonization, for example.
  • Maximum Circularity: a morphological threshold used to distinguish between mycelial objects and unwanted artifacts. Objects with a circularity greater than this threshold value are not analysed.
  • Minimum Area: the minimum area (in microns) required for an object to be analysed and included in the results.

Output Data

  • Projected Area (Ap): the projected area of an object in square microns.
  • Circularity (C): defined as C = Ap(4 * P^2), where P is the length of the perimeter.
  • Total Length (Lh): the total length (in microns) of a mycelial structure. This length is estimated from skeletonised, binary representations of each mycelium.
  • Number of End-Points (N): the number of hyphal tips on a mycelial structure, estimated as the number of 'end-points' on a skeletonised, binary representation.
  • Number of Branch-Points (Nb): the number of hyphal branches on a mycelial structure, estimated as the number of crossing points on a skeletonised, binary representation.
  • Hyphal Growth Unit (Lhgu): the number of hyphal tips per unit length, Lhgu = Lh / N.
  • Box-Counting Fractal Dimension (Db): a measure of the 'self-similarity' of an object, or the degree to which a mycelium fills space.
  • Fourier Fractal Dimension (Df): An alternative form of the fractal dimension, evaluated based on an analysis of the spectral periodicity of the mycelial boundary, which has been shown to correlate strongly with branching frequency.
  • Lacunarity (L): defined as L = |<sigma>^2 / x^2 - 1.0|, where where x is the average luminance value of pixels within an object boundary (so internal 'holes' are included) and <sigma>^2 is the variance of the luminance values. This represents an estimation of how a mycelium fills space.

Advanced Settings

  • Light/Dark Background: specifies whether the images consist of dark objects against a white background (typical of conventional light microscopy) or light objects against a dark background (typical of fluorescence microscopy).
  • Remove Uneven Background: if ticked, background correction will be performed using ImageJ's implementation of the 'Rolling Ball' algorithm. Generally, background correction is desirable, but can have unexpected effects if large, dense mycelia are being analysed.
  • Maximum Object Size: estimated size of the largest object in the image. This parameter is used to determine the radius of the rolling ball filter for background subtraction.
  • Separate Touching Objects: perform watershed segmentation to break apart touching objects. This can be useful for when analysing images of fungal spores or pellets, for example.
  • Create Mask Images: if ticked, a folder (as a subdirectory within the folder containing the analysed images) will be created containing a binary image of each object analysed, together with a skeletonised version, with branch-points and end-points highlighted:

Input.png Output.png

  • Analyse Whole Image: if selected, AnaMorf will not attempt to isolate individual objects in each image. Instead, the entire field of view will be treated a single object to be analysed.
  • Exclude Edge Objects: exclude from the analysis any object in contact with an edge of the image.
  • Auto/Manual Threshold & Manual Grey Level Threshold: a manual value can be entered for grey-level image segmentation, or segmentation can be performed automatically using one of ImageJ's thresholding algorithms.