Contents

Microtubule end tracking in live cell fluorescent images of Drosophila oocyte involves overcoming the following challenges, which can be tackled by a series of preprocessing steps and tracking described in Parton et al (2011)

Simple workflow description for ImageJ, step-by-step description for delineating focal adhesions, count and characterize their positions.  

Measurement of dynamics is not involved.

The workflow includes segmentation, tracking and quantifying morphological dynamics of moving cells in 3D. The authors have implemented the workflow in Matlab, but so far there is no download link provided. To apply this workflow, we recommend to contact the authors or to implement the worflow based on the detailed description in the original paper.

This macro and plugins suite for ImageJ (and Fiji) serves to measure the velocity of moving structures and visualize them, from image time series (2D over time).

The module can be installed in ImageJ as a Macro Menu and each function/component can be called separately. The full workflow consists in calling some, or all, the functions sequentially in order to get from the image preparation (e.g. filtering and visualization of tracks) to the production of the kymographs (time vs. distance plot) and their analysis (retrieving the velocities).

WASH, Exo84, and cortactin spot detection and codistribution analysis To detect endosomes, an automatic Otsu threshold is applied to the Gaussian-filtered MT1-MMP–positive endosome image (= 1.5 pixels for the sample image). Statistics about each endosome are then saved, for example random positioning of spots can be compared to actual positioning. For each endosome, WASH and Exo84 (or WASH and cortactin) spots are searched for in a neighboring of x pixels in their respective channel.

This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).

Sample image data is available in the documentation page. 

Simple macro to separates blobs.

• Load the ImageJ sample image "Blobs"
• Run the plugin Morphological Segmentation
• Display the overlaid

This macro depends on "Morphological Segmentation" component of the MorphoLibJ library, which should be installed via update sites.

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.

An ActionBar is a simple annotated text document that has snippets of Imagej macros or Beanshell arranged into buttons. This tool is very useful when creating custom work flows integrating multiple components. Each component can be linked to a button for a more streamlined and accessible workflow.

It explains how to use ImageJ to compare the density (aka intensity) of bands on an agar gel or western blot.

Some notes can be found here: http://cellnetmcweb.bioquant.uni-heidelberg.de/image-analysis/ShortTutorials/Fiji_GelAnalyzer.pdf

The workflow contains a Matlab package (plusTipTracker) for segmentation and tracking of microtubule tips, based on fluorescence time-lapse movies from microtubule tip markers such as EB-GFP. The tracking model accounts for the specific movement characteristics of microtubules Moreover, scripts for secondary analysis of detected microtubule paths are provided.

plusTipTracker is part of u-track 2.0 package. The workflow is described in the reference. 

An exponential curve fitting library used for Fluorescence Lifetime Imaging (FLIM) and Spectral Lifetime Imaging (SLIM), available as:

• Source code as C/C++ and Java library

• ImageJ plugin

• Standalone Open-Source Windows application

Publications:

## Features >The IJBlob library indentifying connected components in binary images. The algorithm used for connected component labeling is: >Chang, F. (2004). A linear-time component-labeling algorithm using contour tracing technique. Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002 ##Reference Wagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. Journal of Open Research Software 1(1):e6, DOI:

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

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

A menu item in ImageJ that allows you to inspect a 3D stack with orthogonal views (XY, XZ, YZ planes). Slicing plane could be interactively moved by dragging crosses. Pedro Almada wrote a plugin to save the current orthogonal views as montage. ## ImageJ Macro Usages [Save orthogonal views | OrthoSaver](http://uic.igc.gulbenkian.pt/macros/OrthoSaver.ijm) >ImageJ's orthogonal viewer for 3D stacks doesn't let you easily save the current orthogonal view.