Free and open source

Web based viewer developped for google for very big data: 

Neuroglancer is a WebGL-based viewer for volumetric data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). The segmentation has to be done before loading the dataset, it is not done Inside the viewer.

This is not an official Google product.

It has among other the nice feature of beeing able to generate url for sharing a specific view.

Note that the only supported browser for now are 

• Chrome >= 51
• Firefox >= 46

We have developed WormScan, an automated image acquisition system that allows quantitative analysis of each of these four phenotypes on standard NGM plates seeded with E. coli. This system is very easy to implement and has the capacity to be used in high-throughput analysis.

Protein array is used to analyze protein expressions by screening simultaneously several protein-molecule interactions such as protein-protein and protein-DNA interactions. In most cases, the detection of interactions leads to an image containing numerous lines of spots that will be analyzed by comparing tables of intensity values. To describe the observed different patterns of expression, users generally show histograms with the original associated images [1]. The “Protein Array Analyzer” gives a friendly way to exploit this type of analysis, thus allowing quantification, image modeling and comparative analysis of patterns.

The Protein Array Analyzer, which was programmed in ImageJ’s macro language, is an extention of the Dot Blot Analyzer, [2], [3] a graphically interfaced tool that greatly simplifying analysis of dot arrays.

Multi-template matching can be used to localize multiple objects using one or a set of template images.

Contrary to previous implementations that allow to use only one template, here a set of templates can be used or the initial template(s) can be transformed by rotation/flipping.

Multiple objects detection without redundant detections is possible thanks to a Non-Maxima Supression relying on the degree of overlap between detections.

The solution is available as a Fiji plugin (Multi-Template Matching AND IJ-OpenCV update sites), as a Python package (Multi-Template-Matching on PyPI) and as a KNIME workflow (via KNIME Hub).