Contents

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.

These two similar KNIME workflow solutions take 3D data stacks to segment the spots first, using local thresholding with subsequent morphological operations in order to remove noise. Colocalization is then defined by overlapping or center point distance between segmented objects. Further filtering such as overlapping ratio or distance range is done through KNIME table processing.

Two different types are available. 

The overall colors seen in H&E stained slides can vary widely, influenced by factors such as the precise stains and scanner used. This MATLAB function implements the color normalization strategy described in Macenko et al (2009) in order to match stain colors in an image more closely to 'reference' stains. This may help when comparing images visually, or when applying an automated analysis algorithm.

The function may also be useful to understand the functioning of the color deconvolution described in Ruifork and Johnston (2001).

u-Track is a client-side OMERO MATLAB application implementing the sophisticated multiple-particle tracking algorithm of Jaqaman et al. . It works on data previously imported into an OMERO server, and produces results in the form of MATLAB data structures as well as providing functionality to visualise these results.

This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.

Documentation starts from p23 of the linked PDF. 

Example Image: mitocheck_small.tif (2.9M)

Quantification of HER2 immunohistochemistry.

ImmunoMembrane is an ImageJ plugin for assessing HER2 immunohistochemistry, described in [bib]2472[/bib]. It is important to read the URL documentation and original paper to understand how to use the plugin appropriately.

There is web service available. Users can upload image data to process them and get cell membrane to be segmented: Web ImmunoMembrane

These two KNIME workflow solutions are similar: first one detects nuclei and spots inside the nuclei without taking care of surrounding regions, i.e. mitochondria. The second one provides the full solution including spots in mitochondria.

see section 2.4 for KNIME workflow. Section 2.3 is also available, using Fiji. 

The article describes how a FRAP experiment can be conducted and subsequently analyzed. This includes steps in ImageJ and subsequent normalization of the intensity data.

This is a qualitative analysis, and curve fitting is done using Excel. 

Requires "Template matching and Slice alignment plugin"

For each ROI, provides the ratio of pixels over a given threshold over the total number of pixels in the ROI.

Very simple application that lets you load your time-lapse intensity data to generate the normalized FRAP recovery curve and perform exponential curve fitting.

This protocol perform a median filter on the active sequence using the ImageJ rank filter plugin. Then, it converts the result back into Icy for display.

An example showing passing data between ICY and ImageJ using ImagePlus object. 

This tool adds to ImageJ functions to build and organize montages. It comes with the ImageJ installer but can also be downloaded from the ImageJ wiki. A video tutorial is available.

OMERO is an image database application consisting of a server and several clients, the most important of which are the web client and _Insight_ java application. Metadata are extracted from images that have been imported (either using the Insight client, or directly from the filesystem), and this is accessible for search. A standardised hierarchy of _Project > Dataset > Image_ in which image thumbnails can be viewed, combined with group membership, tagging, and attachment of results and other files gives a powerful framework for organising scientific image data.

CellX is an open-source software package of workflow template for cell segmentation, intensity quantification, and cell tracking on a variety of microscopy images with distinguishable cell boundary.

Installation and step-by-step usage details are described in Mayer et al (2013). 

This macro recognizes wells in a picture from a multi-well plate (it works also on a picture of a single well). It is used to segment a picture to determine the number of "Colony Forming Units" in each individual well of a plate.

The steps are the following:

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.

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Image processing library for Python >The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a versatile set of image processing routines. It is written in the Python language. This SciKit is developed by the SciPy community. All contributions are most welcome!

This protocol takes a folder containing images as input and extract each channel in a separate sub folder.