Image thresholding

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 tool measures the total length of the microtubules in a 3D image.

See: http://dev.mri.cnrs.fr/projects/imagej-macros/wiki/Microtubules_Tool_(3…

You can find a test image here.

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.

ITK is an open-source, cross-platform system that provides developers with an extensive suite of software tools for image analysis.

Developed through extreme programming methodologies, ITK employs leading-edge algorithms for registering and segmenting multidimensional data. It is widely used and contributed in the medical imaging field.

Strengths

Highly optimized C++, well commented Consistently updated (new) algorithms many tools and softwares are built upon it connected with VTK Insight Journal (open code and sample data) Extensive list of examples & tutorials

Limitations

yet detached from the bioimage analysis world hard to use for end users without development skills

This library gives the numpy-based infrastructure functions for image processing with a focus on bioimage informatics. It provides image filtering and morphological processing as well as feature computation (both image-level features such as Haralick texture features and SURF local features). These can be used with other Python-based libraries for machine learning to build a complete analysis pipeline.

Mahotas is appropriate for users comfortable with programming or builders of end-user tools.

==== Strengths

The major strengths are in speed and quality of documentation. Almost all of the functionality is implemented in for multiple dimensions. It can be used with other Python packages which provide additional functionality.

Mahotas and all packages on which it relies are open-source.