Image segmentation

ilastik is a simple, user-friendly tool for interactive image classification, segmentation and analysis. It is built as a modular software framework, which currently has workflows for automated (supervised) pixel- and object-level classification, automated and semi-automated object tracking, semi-automated segmentation and object counting without detection. Most analysis operations are performed lazily, which enables targeted interactive processing of data subvolumes, followed by complete volume analysis in offline batch mode. Using it requires no experience in image processing.

ilastik (the image learning, analysis, and segmentation toolkit) provides non-experts with a menu of pre-built image analysis workflows. ilastik handles data of up to five dimensions (time, 3D space, and spectral dimension). Its workflows provide an interactive experience to give the user immediate feedback on the quality of the results yielded by her chosen parameters and/or labelings.

The most commonly used workflow is pixel classification, which requires very little parameter tuning and instead offers a machine learning technique for segmenting an image based on local image features computed for each pixel.

Other workflows include:

Object classification: Similar to pixel classification, but classifies previously segmented objects by object characteristics in a subsequent step

Autocontext: This workflow improves the pixel classification workflow by running it in multiple stages and showing each pixel the results of the previous stage.

Carving: Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds

Manual Tracking: Semi-automated cell tracking of 2D+time or 3D+time images based on manual annotations

Automated tracking: Fully-automated cell tracking of 2D+time or 3D+time images with some parameter tuning

Density Counting: Learned cell population counting based on interactively provided user annotation

Strengths: interactive, simple interface (for non-experts), few parameters, larger-than-RAM data, multi-dimensional data (time, 3D space, channel), headless operation, batch mode, parallelized computation, open source

Weaknesses: Pre-built workflows (not reconfigurable), no plugin system, visualization sometimes buggy, must import 3D data to HDF5, tracking requires an external CPLEX installation

Supported Formats: hdf5, tiff, jpeg, png, bmp, pnm, gif, hdr, exr, sif

Semi-automated segmentation of 3D objects (e.g. neurons) based on user-provided seeds.

Image segmentation based on the MOSAIC Discrete region competition algorithm. 

This plugin can be used for inferring spatial interactions between patterns of spot-like objects in images or between coordinates read from a file. 

WIS-PhagoTracker is a software application for quantitative analysis of high throughput cell migration assay. The cell migration assay is based on a modified Phagokinetic tracks procedure, in which motile cells "leave their tracks" on a specialized surface. These tracks are visualized using a screening microscope.

WIS-PhagoTracker enables morphometric analysis of such tracks. It uses multiscale segmentation algorithm for fine detection of tracks and cells boundaries.

Following the segmentation step, it quantifies various morphometric parameters for each track, such as track area, perimeter, major and minor axis and solidity. All these measures are calculated for each track in each well of a well plate and saved for further statistical analysis WIS-PhagoTracker supports all the analysis phases starting from preprocessing, finding tracks of selected wells or a whole plate, through viewing the results and manually rejecting tracks to statistical analysis of the results. It also supports batch processing of several plates, and analysis of single image files. A user interface enables the user to modify the relevant parameters of the process, according to specific image's requirements.

Results are exported into Excel readable files.

WIS-NeuroMath - is a software tool for automated analysis and quantification of fluorescent microscopy images of Nerve cells, in both in vivo and in vitro preparations. It allows for accurate detection of neurites in challenging images. Following neurite detection, different types of processing can be carried: Cell Morphology of cultured neurons, Neurite Length Analysis and Ganglion Explant Analysis. Usefull also for angiogenesis analysis.

This plugin threshold an image using the Maximum Entropy algorithm, which aims at maximizing the inter-class entropy. Entropy is defined as -sum(p.*log2(p)), where p contains the histogram bin counts. This thresholding is very useful to segment images with few bright objects on large dark background. In ImageJ/FIJI you can acces this tool in Image->Adjust->Threshold and choose in the list In Aphelion, you can access this tool in Seglmentation->Threshold-> AphImgEntropyThreshold

Drop Shape Analysis is a collection of two methods (DropSnake and LBADSA) for high-accuracy measure of contact angles for drop measurement.

Image segmentation by representative colors selection. Two versions are available :

• thresholding
• positive and negative colors selection and SVM learning

IdentifySecondaryObjects identifies objects (e.g., cells) using objects identified by another module (e.g., nuclei) as a starting point.

This plugin extracts groups of connected pixels in 2D and 3D based on their intensity and that of the background. Works on both binary and gray-scale data. Output can be pushed to the swimming pool for other plug-ins to further exploit the extracted objects.

Plugin designed to allow easy semi-automatic tracing of neurons or other filament-like structures (e.g., microtubules, blood vessels) through either 2D images or 3D image stacks. Data can be imported and exported in SWC files for interaction with other software, or details of the traces can be exported as CSV files for analysis in spreadsheets or statistical software.

This plugin is included in Fiji by default.

Plugins for 3D Image processing and Analyisis in ImageJ. Previously (?) known as 3D ImageJ Suite.

This segmentation method performs a N-class thresholding based on a K-Means classification of the image histogram, then extracts objects in a bottom-up manner using user-defined minimum and maximum object sizes. Very useful to detect clustered objects in fluorescence microscopy.

Identify objects (as nuclei) within an image without needing the assistance of another cellular feature (as cell). 

MembraneQuant performs automatic evaluation on IHC membrane stainings (HER2, EGFR etc).

Using color deconvolution, MembraneQuant detects cell membrane and measures staining intensity on the chromogen channel. This way it is possible to calibrate the software to the actual stain protocol in the pathology lab. The algorithm categorizes the detected membrane to weak positive, medium positive and strong positive classes.

This software has an IVD certification for HER2 quantification.

This is an icy package that encapsulates tools to design and implement parametric active contours. The package provides fast 2D and 3D filters for image preprocessing, and a framework to create and evolve snakes defined by a set of control points.

This software implements an active contour (a.k.a. snake) segmentation method using exponential splines as basis functions to represent the outline of the shape. While the snake is versatile enough to provide a good approximation of any closed curve in the plane, its most important feature is that it perfectly reproduces circular and elliptical shapes. These features are very appropriate to delineate cross sections of cylindrical-like conduits and to outline blob-like objects.

Choose the best auto thresholding technique for your data. 

This module lets you outline the objects in an image using the mouse.

The Graph Cut plugin provides a way to obtain a globally smooth binary segmentation. As input, you have to provide a gray-scale image that represents the pixel affinities for belonging to the foreground. Via a single parameter you can adjust the smoothness of the segmentation.

The Trainable Weka Segmentation is a Fiji plugin that combines a collection of machine learning algorithms with a set of selected image features to produce pixel-based segmentations. Weka (Waikato Environment for Knowledge Analysis) can itself be called from the plugin. It contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to this functionality. As described on their wikipedia site, the advantages of Weka include: - freely availability under the GNU General Public License - portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform - a comprehensive collection of data preprocessing and modeling techniques - ease of use due to its graphical user interfaces - Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection.

The main goal of this plugin is to work as a bridge between the Machine Learning and the Image Processing fields. It provides the framework to use and, more important, compare any available classifier to perform image segmentation based on pixel classification.