classification of hemp fibers based on morphological features

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Workflow Author: 

David Legland

Workflow Language: 
Input data type: 
2D or 3D images, grayscale image
Output data type: 
csv file or newly labelled image
Comment / Instructions: 

In this workflow, you can use MorphoLibJ to generate accurate morphometric measurements

  • First the fibers are segmented by mathematical morphology: for example by using MorphoLibJ:

    Create a marker image by creating a rough mask with extended regional maxima (similar to Find Max), such that you have one max per fiber
    Use the marker controlled watershed (in MorpholLibJ/ Segentation/ marker controlled watershed) : indicate the orginal grayscale image as the input, Marker will e your maxima image, select None for mask
    it will create a label mask of your fibers

  • In MorphoLibJ /A analyze/ select Region Morphometry: this will compute different shape factors which are more robust than the ones implemented by default in ImageJ

  • Export the result table created to a csv file

  • Then for example in Matlab or R, you can apply a PCA analysis (Principal component analysis) followed by a k-means with the number of class (clusters) (different fibers type) you want to separate.

  • You can then add this class as a new feature to your csv file.

  • From this you can sort your labelled fibers into these clusters for a visual feedback or further spatial analysis


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