Microscopy

This imageJ/Fiji plugin provides an analysis of the granulometry inside an image by mathematical morphology. It has sevral option for the structuring element to be used, and the size domain to be tested. The output will be both a curve of the remaining content of the image against the growing size of the structuring element, and the corresponding results table that could be then exported. It can deal with grayscale images directly, no need to segment the image first. This plugin can then be used to compare different texture based on some statistical analysis of the produced curve (for exemple comparison of the geometrical means to discriminate 2 textures). It is macro recordable as well. Programming Language: java Processes: successive erosion, dilation, closing or opening -> ANALYSIS User skills: Life Scientist, developers, analysts

MorphoLibJ is a library of plugin for ImageJ with functionalities for image processing such as filtering, reconstructing, segmenting, etc... Tools are based on Mathematical morphology with more rigorous mathematical approach than in the standard tools of ImageJ in particular for surface (or perimeter) measurements which are usually based on voxel counting.  

http://imagej.net/MorphoLibJ#Measurements

Among the features:

Morphological operations :  Dilation, Erosion, Opening,  Closing , Top hat (white and black), Morphological gradient (aka Beucher Gradient), Morphological Laplacian, Morphological reconstruction, Maxima/Minima , Extended Maxima/Minima -Watershed (classic or controlled) -Image overlay -Image labelling -Geodesic diameter -Region Adjacency Graph -Granulometry curves, morphological image analysis.

Evaluates the orientation of fiber orientation pattern and plots the results in the image. It calculates gradient in x and y direction. - then calculates the eigenvector of nematic tensor, which is the orientation of the pattern.

An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.

Variational algorithms to remove stationary noise. Application to microscopy imaging. This plugin allows to denoise images degraded with stationary noise. Stationary noise can be seen as a generalization of the standard white noise. Typical applications of this plugin are:

- Standard white noise denoising using a total variation and fidelity term minimization. Even though total variation denoising is not the state of the art (regarding SNR improvement), it may be very valuable for further tasks such as image seg- mentation).

- Destriping (the problem that motivated us to develop these ideas). 

- Deconvolution (even though most users won't be able to use this feature).

- Cartoon + texture decomposition which might be useful to compress images, analyse textures or simplify segmentation like tasks.