MIPAV

Description

The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

2-D Colocalisation in Cells

Description

The workflow computes cell-based colocalisation of two stainings in 2-D images. Both pixel- and object-based readouts are provided and some pros and cons are discussed. Please read here for more information:

https://github.com/tischi/ImageAnalysisWorkflows/blob/master/CellProfil…

 

Input data type: 

images

Output data type: 

processed images, numbers, text file, csv files

need a thumbnail

Leaf Infection Tools

Description

The Leaf Infection Tools allow to measure the area of leaves, of two stainings in different channels and of the overlap region of the two stainings. 

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

Test image: http://biii.eu/node/1143

a leaf with infection pattern

CellProfiler Examples - Colocalization

Description

Quote:

Measuring the colocalization between fluorescently labeled molecules is a widely used approach to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identification and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels.

Sample image is included in the download package.

spot detection and codistribution analysis

Description

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. Their number and position are saved per endosome (**see the macro in Text file S2 downloadable from here**).

From the position of WASH and Exo84 (or WASH and cortactin) spots around each endosomes, each WASH spot is paired with its closest Exo84 (or cortactin) spot neighbor, optimized over all spots around this endosome.

This allowed measuring of the distribution of distance between WASH-Exo84 (or WASH-cortactin) spots (**for the co-distribution analysis, see matlab scripts in Zip file S3 downloadable).

endosomes and spot neighbors