Automated

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. 

Sample image: hela-cells.tif (674k x 3)

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.

Quote: The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.

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. 

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. Images can also be analysed server-side or client-side within the base OMERO application or one of its many extensions. OMERO has APIs for extension in multiple languages: java, python, C++ and MATLAB; and such extensions have easy access to the image data and metadata in the database.

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). 

After users provide a few annotations of cell sizes and cell boundary profiles, it tries to match boundary profile pattern on cells thus provide segmentation and further tracking. It works the best on cells without extreme shapes and with a rather homogeneous boundary pattern. It may not work well on images with cells of sizes only a few pixels. Its output comprises control images for visual validation, text files for post-processing statistics, and MATLAB objects for advanced subsequent analysis.

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:

1. Makes a 8-bit B&W picture, inverts it (=> borders will look white instead of black), resizes it (optional, this is to speed-up convolution thereafter) and find edges.
2. Convolves the obtained picture with a kernel corresponding to a thick white circle of the size of the wells. The resulting image has big "blobs" or "particles" corresponding roughly to the centers of the well.
3. The image is thresholded to remove particles not corresponding to strong hits and "Analyze particle" is run.
4. The measured parameter is the center of mass of the particles which gives the center of the well. These are saved in an array.
5. Circles are drawn and added to the ROI manager. The centers of the circles are the identified centers of mass of the particles and their radius is the expected radius of the wells in the original image.

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.

This method allows to compute a threshold that preserves the moments of an image. In ImageJ/Fiji, you can access it in Image->Ajust->Threshold and choose Moments in the list. In Aphelion, the tool is in Segmentation->Threshold->AphImgMomentThreshold The original paper is 2449

Tracks a cell in a 2D video using active contours, and produces a list of ROI where intensity is measured and reported into a workbook. The cell must be first delineated with a ROI in the first image of the video.

A utility macro for the specified use of BioFormats plugin. Takes a folder of proprietary images formats (Zeiss zvi, lsm, czi or Nikon nd2) and extracts them to .tif images

The extracted images are located in a folder defined in the menu. Other options: reset spatial scales, reads ROIs, split channels, add stage position in the name.

note: The old name of the macro was "ZVI Extractor" and the data format was limited to ZVI, but the upgraded version includes more formats.

Requires Bio-Formats plugin

This protocol computes the optical flow of a 2D+T sequence. The results are displayed with flow arrows painted on top of the original sequence, and also with two additional sequences for the norm of the flow and a color-coded presentation of the flow following the Middlebury convention.

This macro is similar to the LIF_Extractor, but it will output Z-projection of each Z-stack. You can choose the type of projection in addition to the other options. Requires Bio-Formats plugin

Download the protocol,use and modify in Icy. It permits to detect spot with wavelet spot detector block. Input : loop on a folder Outputs: excel, binary, and detection screenshot

This macro extracts .lei and .lif multichannel Z-stacks into multiple .tif stacks, splitting the channels into different stacks. Several options are possible such as background substraction, various filters, or optional reset of spatial scale. Requires Bio-Formats plugin

>OpenSlide is a C library that provides a simple interface to read whole-slide images (also known as virtual slides). Python and Java bindings are also available. The Python binding includes a Deep Zoom generator and a simple web-based viewer. The Java binding includes a simple image viewer.  

This workflow classifies, or segments, the pixels of an image given user annotations. It is especially suited if the objects of interests are visually (brightness, color, texture) distinct from their surrounding. Users can iteratively select pixel features and provide pixel annotations through a live visualization of selected feature values and current prediction responses. Upon users' satisfaction, the workflow then predicts the remaining unprocessed image(s) regions or new images (as batch processing). Users can export (as images of various formats): selected features, annotations, predicted classification probability, simple segmentation, etc. This workflow is often served as one of the first step options for other workflows offered by ilastik, such as object classification, automatic tracking.

Illumination correction is often important for both accurate segmentation and for intensity measurements. This example shows how the CorrectIlluminationCalculate and CorrectIlluminationApply modules are used to compensate for the non-uniformities in illumination often present in microscopy images.

In this example, cells are grown as a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the area occupied by the tissue sample.

Download package also contains example images.