ImageJ Macros

Wound healing assay: analysis in ImageJ

Workflow
Description

This macro was designed to measure the size of the scratch wound in a wound scratch assay. It uses an edge-detection and thresholding technique.

It will batch process all images in a directory. Images captured by time-lapse should be compiled into stacks using a tool similar to "Metamorph nd & ROI files importer (nd stack builder)" by Fabrice P. Cordelières. Images to be analyzed should be placed in one directory (Source Directory). A second directory should be created to save results files and images (Destination Directory). Setting correct Lower and Upper thresholds is important to obtain a good result. Two macros are available, one using edge detection, the second one using background subtraction.

has function
Image segmentation
Wound-healing analysis

Counting Clumped Cells

Workflow
Description

A workflow combining ImageJ macro and manually using Trainable Weka Segmentation plugin for counting clumped cells.

has function
Cell segmentation
Object counting

Histo_surfaces

Workflow
Description

An ImageJ macro for calculating empty surfaces on histological slices (ex: tubules in a kidney).

has topic
Digital histology

Centromere Recognition and Quantification

Workflow
Description
<p>Particle detection is based on "Analyze Particles" in ImageJ. It probably could also be used in spot detection, not limited to centromere. &gt;This macro is described in Bodor et al. (2012). The macro recognizes centromere or kinetochore foci in Delta Vision or TIFF images and determines their centroid position. Fluorescent intensities are then measured for each centromere by placing a small box around the centroid position of the centromere. The peak intensity value within the box is corrected for local background by subtraction of the minimum pixel value. This process results in an accurate measurement of large numbers of centromere or kinetochore-specific signals. Following papers uses CRaQ (picked up, maybe more): - [Fachinetti et al. (2017)](https://www.cell.com/developmental-cell/pdf/S1534-5807(16)30909-1.pdf), Developmental Cell 40, 104–113, - [Guo et al. (2017)](https://www.nature.com/articles/ncomms15775) Nature Communications volume 8, Article number: 15775 (2017) doi:10.1038/ncomms15775 - [Lgosdon et. al. (2015)](http://jcb.rupress.org/content/208/5/521) J Cell Biol Mar 2015, 208 (5) 521-531; DOI: 10.1083/jcb.201412011 - [Bodor et al. (2014)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091408/), eLife. 2014; 3: e02137</p>
has topic
Fluorescence microscopy
has function
Spot detection

Arabidopsis Seedlings Tool

Workflow
Description

The Arabidopsis Seedlings Tool allows to analyze the germination and seedling growth of Arabidopsis (Arabidopsis thaliana) in liquid culture. It measures the surface of green pixels per well in images containing multiple wells. It can be run in batch mode on a series of images. It writes a spreadsheet file with the measured area per well and saves a control image showing the green surface that has been detected per well. 

See http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Arabidopsis_Seedlings_Tool

Test images can be found here.

has topic
Bioimage informatics
has function
Image thresholding
ImageJ toolbar of the arabidopsis seedlings tool

Manual Frame Drift Registration

Component
Description

An ImageJ macro for correcting frame drift occurred during image acquisition.

It often happens that you have an image sequence that shows problematic drifting of image frame and at the same time you have some landmarks that could be used for correcting the drift. This ImageJ macro allows you to Manually track the landmark using ImageJ Manual Tracking Plugin. Using the coordinates recorded in the Result window, each frame is shifted back so that the landmark stays in a single place.

has topic
Fluorescence microscopy
has function
Image registration
Drift correction
Image correction

Dithering

Component
Description

Dithering is a type of half tone thresholding where greyscale (or RGB channel) intensity is converted into a local density of binary pixels. This is ideal for rendering images in devices with a binary output such as printers (greyscale) or with a small number of colours (colour dithering). The following methods have been implemented (there are several more):

  • Floyd-Steinberg
  • Atkinson
  • Jarvis-Judice-Ninke
  • Stucki
  • Bayer_2x2
  • Bayer_4x4
  • Bayer_8x8
  • Clustered_4x4
  • Random

Here is a good text explaining various dithering algorithm.

has topic
Bioimage informatics
has function
Conversion
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