Manual

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.

This article Baslat et al. presents a method to compute Lymphatic Vessel Density on an image of the whole slide (a workflow documented as text).

Vessels are obtained with a Maximum Entropy Thresholding applied on the excess Red channel (2 times the red values minus blue+green value). Stroma tissue is obtained with a Moment Preserving Thresholding on the blue channel.

OMERO.figure is an OMERO web application that makes generating figures from images in an OMERO image database very quick and easy. The images in the figure link back to the original data, greatly simplifying the process of adjusting the view and keeping track of original data. PDF documents are generated, which can be opened using e.g. Adobe Illustrator or Inkscape in order to produce the final finished figure.

The Fiji distribution of ImageJ comes with several manual tracking tools, two of which are particularly useful:

* _Plugins->Tracking->Manual Tracking_

* _Plugins->Tracking->Manual tracking with TrackMate_ (TrackMate is an advanced automatic tracking tool, with the option for manual editing of tracks)

The _Manual Tracking_ plugin is quick to use, intuitive and produces easy-to-understand output. TrackMate has the advantage that automatic detection and linkage can be combined with manual input.

Update sites

MtrackJ (see the component page here) can be installed via Fiji update sites. It has many shortcut keys enabled so for manually tracking many data, it will become quite efficient as you get used to the short-cut key operation.

Pre-processing

Pre-processing steps before manual tracking might include:

* denoising and/or deconvolution

* flicker and photobleaching correction, e.g. using Fiji's _Image->Adjust->Bleach Correction_

* flat-field correction, and/or bandpass (ImageJ's _Process->FFT->Bandpass filter_) according to the size of the features of interest

Various ways are proposed in different websites for example:

• Count the number of foci
• IJ Forum: How to utilize ImageJ to count foci per nucleus
• PDF: Two Ways to Count Cells with ImageJ

Here, a workflow template using ImageJ's build-in Find Maxima ( Process -> Find Maxima) is explained. It can be used for many 2D counting-related tasks.

For counting small, bright foci (dots), set Output type to be Point Selection. If too many points are detected, the number may be reduced using one or more of the following methods:

Apply a filter to reduce noise, e.g. Process -> Filters -> Gaussian Blur... prior to running Find Maxima Set a minimum threshold with Image -> Adjust -> Threshold... prior to running Find Maxima, then use the Above lower threshold option within the dialog box Increase the Noise tolerance value (which effectively acts as a local threshold)

The resulting point selection can be modified (points added/removed) by the Multi-Point tool.

After the points are available, final measurements can be made using Analyze -> Measure.