Align slices in stack


Align_slices in stack utilized the template matching function cvMatch_Template to do slice registration(alignment) based on a selected landmark.
This function will try to find the landmark or the most similar image pattern in every slice and translate each slice so that the landmark pattern will be the same position throughout the whole stack. It could be used to fix the drift of a time-lapse image stacks.

Source code: link

Input data: image stack
output data: image stack

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Template Matching and Slice Alignment--- ImageJ Plugins


This ImageJ plugin contains two functions. The first one is the cvMatch_Template. It implements the template matching function from the OpenCV library. The second function Align_slices in stack utilized the previous matching function to do slice registration(alignment) based on a selected landmark. 

For more details, refer to the page of each component. 


Align Slices in Stack

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Elastix is a toolbox for rigid and nonrigid registration of (medical) images.

Elastix is based on the ITK library, and provides additional algorithms for image registration. 

The software can be run as a single-line command, making it easy to include in larger scripts or workflows. The user needs to edit a configuration file that contains all relevant parameters for registration: transformation model, metric used to comapre images, optimization algorithm, mutliscale pyramidal representation of images...

Nowadays elastix is accompanied by SimpleElastix, making it available in other languages like C++, Python, Java, R, Ruby, C# and Lua.

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Spark Stitcher


Reconstruct big images from overlapping tiled images on a Spark cluster.

The code is based on the Stitching plugin for Fiji




Working version of a simple GUI frontend for CMTK image registration tools in Fiji

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The MIPAV (Medical Image Processing, Analysis, and Visualization) application enables quantitative analysis and visualization of medical images of numerous modalities such as PET, MRI, CT, or microscopy. Using MIPAV's standard user-interface and analysis tools, researchers at remote sites (via the internet) can easily share research data and analyses, thereby enhancing their ability to research, diagnose, monitor, and treat medical disorders.

ANTs: Advanced Normalization Tools


ANTs computes high-dimensional mappings to capture the statistics of brain structure and function.

Image Registration

Diffeomorphisms: SyN, Independent Evaluation: Klein, Murphy, Template Construction (2004)(2010), Similarity Metrics, Multivariate registration, Multiple modality analysis and statistical bias

Image Segmentation

Atropos Multivar-EM Segmentation (link), Multi-atlas methods (link) and JLF, Bias Correction (link), DiReCT cortical thickness (link), DiReCT in chimpanzees


Advanced Normalization Tools



A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O.

The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction; EPI unwarping), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear model).

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TeraStitcher is a free tool that enables the stitching of Teravoxel-sized tiled microscopy images even on workstations with relatively limited resources of memory (<8 GB) and processing power. It exploits the knowledge of approximate tile positions and uses ad-hoc strategies and algorithms designed for such very large datasets. The produced images can be saved into a multiresolution representation to be efficiently visualized (e.g. Vaa3D-TeraFly) and processed.

2D brain slice region annotation: SliceMap



Whole brain tissue slices are commonly used in neurobiological research for analyzing pathological features in an anatomically defined manner. However, since many pathologies are expressed in specific regions of the brain, it is necessary to have an annotation of the regions in the brain slices. Such an annotation can be done by manual delineation, as done most often, or by an automated region annotation tool.

SliceMap is a FIJI/ImageJ plugin for automated brain region annotation of fluorescent brain slices. The plugin uses a reference library of pre-annotated brain slices (the brain region templates) to annotate brain regions of unknown samples. To perform the region annotation, SliceMap registers the reference slices to the sample slice (using elastic registration plugin BUnwarpJ) and uses the resulting image transformations to morph the template regions towards the anatomical brain regions of the sample. The resulting brain regions are saved as FIJI/ImageJ ROI’s (Regions Of Interest) as a single zip-file for each sample slice.

More information can also be found in "SliceMap: an algorithm for automated brain region annotation", Michaël Barbier, Astrid Bottelbergs, Rony Nuydens, Andreas Ebneth, Winnok H De Vos, Bioinformatics, btx658,

Example: SliceMaps brain region segmentation