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FOCAL

Collection
Description

FOCAL (Fast Optimized Cluster Algorithm for Localizations) is a rapid density based algorithm for detecting clusters in localization microscopy datasets.

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MIATool

Collection
Description
The Microscopy Image Analysis Tool (MIATool) is a software application designed for the viewing and processing of N-dimensional array of images. At its core is an image viewer which allows the traversal of an N-dimensional array of images. Besides the standard display as pixels of varying intensity values, options are available to view the images as mesh or contour plots. The current version of MIATool supports four different image editing tools which can be used to process the images displayed in the viewer. The intensity adjustment tool provides different ways to modify the pixel intensity values, and the crop tool allows trimming of the images to retain only the portion that is of interest. The two remaining tools - the segmentation tool and the label tool - can be used for manual image segmentation and image labeling.
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Clus-Doc

Collection
Description

Clus-Doc is a software that quantifies both the spatial distribution of a protein as well as its colocalization status. It may be used to quantify signaling activity and protein colocalization at the level of individual proteins.

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Morpholeaf

Component
Description

The MorphoLeaf application allows you to extract the contour of multiple leaf images and identify their biologically-relevant landmarks. These landmarks are then used to quantify morphological parameters of individual leaves and to reconstruct average leaf shapes. MorphoLeaf is developed by the Modeling and Digital Imaging and the Transcription Factors and Architecture teams of the Institut Jean-Pierre Bourgin, INRA Versailles, France, and the Biophyscis and Development group at RDP, Lyon.

has topic
Bioimage informatics
has function
Image registration
Image feature detection

classification of hemp fibers based on morphological features

Workflow
Description

 

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/ Segmentation/ marker controlled watershed) : indicate the original grayscale image as the input, Marker will be your maxima image, select None for mask
      • it will create a label mask of your fibers
  •  In MorphoLibJ /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
has topic
Microscopy
has function
Watershed segmentation
Object classification
Image feature detection
Object feature extraction
Morphological operation
hemp analysis
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