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Smooth 2D Manifold Extraction (SME).

Three-dimensional fluorescence microscopy followed by image processing is routinely used to study biological objects at various scales such as cells and tissue. However, maximum intensity projection, the most broadly used rendering tool, extracts a discontinuous layer of voxels, obliviously creating important artifacts and possibly misleading interpretation. Here we propose smooth manifold extraction, an algorithm that produces a continuous focused 2D extraction from a 3D volume, hence preserving local spatial relationships. We demonstrate the usefulness of our approach by applying it to various biological applications using confocal and wide-field microscopy 3D image stacks. We provide a parameter-free ImageJ/Fiji plugin that allows 2D visualization and interpretation of 3D image stacks with maximum accuracy.

The research goal of this paper was to provide unbiased counts of labeled astrocytes and to estimate the area they cover, further to develop tools for defining the orientation of coupling within astrocyte networks under different stimuli.

In order to count the astrocytes and estimate the area they cover the following steps were used in this software.

Pre-processing: z-project (using max intensity); split channels; subtract background; remove outliers.

Segmentation: adjust threshold and convert to a binary file; Watershed.

Cell counting: Analyze particles

Measure Astrocytic network area: select a ROI using the polygon tool; set measurements (area); ROI manager -> add the traced polygon; measure.

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.