Watershed segmentation

Watershed is the term that commonly refers to a mathematical morphology operation that treats a grayscale image as a topographic map and segments the image. The segmentation is performed by a succesive 'flooding' operation from minima in the image starting from different points and separates the image in different catchment basins.|Needs a comment about the relation between the Watershed and Region growing.

Synonyms
Watershed transformation
Watershed-based segmentation

Nuclei Segmentation 2D watershed (NucSeg3DThr-ImageJ)

Workflow
Description

The macro will segment nuclei and separate clustered nuclei in a 3D image using a 2D Gaussian blur, followed by Thresholding, 2D hole filling and a 2D watershed. As a result an index-mask image is written for each input image.

has topic
Bioimage informatics
has function
Cell segmentation
Watershed segmentation
Morphological operation
need a thumbnail

Nuclei Segmentation (Python)

Workflow
Description

This workflow processes images of cells with discernible nuclei and outputs a binary mask containing where nuclei are detected.

has function
Adaptative thresholding
Watershed segmentation
Object detection
Smoothing
need a thumbnail

Nuclei Segmentation 3D Watershed (NucleiSegmentation3D-ImageJ)

Workflow
Description

The macro will segment nuclei and separate clustered nuclei in a 3D image using a distance transform watershed. As a result an index-mask image is written for each input image.

has function
Image segmentation
Watershed segmentation
Smoothing
need a thumbnail

Watershed (ImageJ ij-1.52i)

Component
Description

Performs watershed algotirhm with ij-1.52i.jar. legacy:ij.plugin.filter.EDM("watershed").

has function
Watershed segmentation
need a thumbnail

Nuclei Segmentation 2D (ImageJ)

Workflow
Description

The macro will segment nuclei and separate clustered nuclei using a binary watershed. As a result an index-mask image is written for each input image.

has function
Image segmentation
Watershed segmentation
need a thumbnail

video tutorial on 3D vessel segmentation of synchrotron phase contrast tomography

Submitted by czhang on Tue, 01/29/2019 - 20:32

In this tutorial video, a coronary arterial tree is used as the demo example to show in detail how the semi-automatic segmentation workflow, Carving from the open-source image analysis software ilastik, can be used. Tips on how and why a preprocessing is done, as well as parameter settings are provided.

Interactive watershed

Component
Description

The interactive Watershed Fiji plugin provides an interactive way to explore local maxima and threshold values while a resulting label map is updated on the fly.

After the user has found a reliable parameter configuration, it is possible to apply the same parameters to other images in a headless mode, for example via ImageJ macro scripting.

has topic
Fluorescence microscopy
has function
Cell segmentation
Interactive segmentation
Watershed segmentation
Spot detection

OpenCV / Image Processing

Collection
has topic
Bioimage informatics
has function
Image feature detection
Image thresholding
Watershed segmentation
Image processing
Geometrical transformation
Linear transformation
Smoothing
Morphological operation

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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