Rigid registration

VTK is an open-source software system for image processing, 3D graphics, volume rendering and visualization. VTK includes many advanced algorithms (e.g., surface reconstruction, implicit modeling, decimation) and rendering techniques (e.g., hardware-accelerated volume rendering, LOD control).

VTK is used by academicians for teaching and research; by government research institutions such as Los Alamos National Lab in the US or CINECA in Italy; and by many commercial firms who use VTK to build or extend products.

The origin of VTK is with the textbook "The Visualization Toolkit, an Object-Oriented Approach to 3D Graphics" originally published by Prentice Hall and now published by Kitware, Inc. (Third Edition ISBN 1-930934-07-6). VTK has grown (since its initial release in 1994) to a world-wide user base in the commercial, academic, and research communities.

Align two images using intensity correlation, feature matching, or control point mapping

Together, Image Processing Toolbox™ and Computer Vision Toolbox™ offer four image registration solutions: interactive registration with a Registration Estimator app, intensity-based automatic image registration, control point registration, and automated feature matching. 

It is an interactive front-end visualization for registration software based on Elasix (VTK/ITK)

Python/C++ port of the ImageJ extension TurboReg/StackReg written by Philippe Thevenaz/EPFL.

A python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame.

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

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.

The Multiview Reconstruction software package enables users to register, fuse, deconvolve and view multiview microscopy images. The software is designed for lightsheet fluorescence microscopy (LSFM), but is applicable to any form of three or higher dimensional imaging modalities like confocal timeseries or multicolor stacks. 

The BigDataViewer is a re-slicing browser for terabyte-sized multi-view image sequences. BigDataViewer was developed with multi-view light-sheet microscopy data in mind and integrates well with Fiji's SPIMage processing pipeline.

The BigStitcher is a software package that allows simple and efficient alignment of multi-tile and multi-angle image datasets, for example acquired by lightsheet, widefield or confocal microscopes. The software supports images of almost arbitrary size ranging from very small images up to volumes in the range of many terabytes, which are for example produced when acquiring cleared tissue samples with lightsheet microscopy.

This is an example workflow of how to perform automatic registration by

- first detecting spots in both images using wavelet segmentation (with different scale according to the image scale)

- second using Ec-Clem autofinder to register both images

Click on a block to know more about a tool. Non referenced tools are non clickable.

quote:

Elastix cite{Klein2010} is an open source, command-line program for intensity-based registration of medical images that allows the user to quickly configure, test, and compare different registration methods. SimpleElastix is an extension of SimpleITK cite{Lowekamp2013} that allows you to configure and run Elastix entirely in Python, Java, R, Octave, Ruby, Lua, Tcl and C# on Linux, Mac and Windows. The goal is to bring robust registration algorithms to a wider audience and make it easier to use elastix, e.g. for Java-based enterprise applications or rapid Python prototyping.

Python example

import SimpleITK as sitk
resultImage = sitk.Elastix(sitk.ReadImage("fixedImage.nii"), sitk.ReadImage("movingImage.nii"))

Automatic registration in 2D or 3D based on detection or binary mask. Takes images with detections already done on it.

This plugin allows to compute a similarity (translation/rotation/scaling and flipping) transform from pair of points. It is updating the transformed image interactively such that the user get immediate feedback. The transformation is saved and can be applied to any other stack/image. Non rigid deformation can also be applied in 2D or 3D.

3D/3D,2D/3D or 3D /2D can be handled .

3D ROI are enabled, and can be checked with the 3D vtk view (size of ROI can be changed using the ROI stroke width).

Some prealignment by rotating in 3D the volume is possible.

Transformations can be applied directly or combined through Block Protocols (search for apply transformation).

It's also provide information about the predicted Error (based on statistical prediction), either as a full color mapping, either on each points used as landmarks, and error on the discrepancy in position between points.

There are video tutorials available in the web.

- 2D Stabilization in each slice of the stacks in time. - 3D Stabilization intravital imaging of all the stacks (including the dimension Z) - create the videos and the stabilized images in a new folder 2701

The ICP algorithm takes two point clouds as an input and return the rigid transformation (rotation matrix R and translation vector T), that best aligns the point clouds. Example: [R,T] = icp(q,p,10); Aligns the points of p to the points q with 10 iterations of the algorithm. The transformation is then applied using R*p + repmat(T,1,length(p)); The file has implemented both point to point and point to plane as well as a couple of other features such as extrapolation, weighting functions, edge point rejection, etc.

Align aligns images relative to each other, for example, to correct shifts in the optical path of a microscope in each channel of a multi-channel set of images.

For two or more input images, this module determines the optimal alignment among them. Aligning images is useful to obtain proper measurements of the intensities in one channel based on objects identified in another channel, for example. Alignment is often needed when the microscope is not perfectly calibrated. It can also be useful to align images in a time-lapse series of images. The module stores the amount of shift between images as a measurement, which can be useful for quality control purposes.

Note that the second image (and others following) is always aligned with respect to the first image. That is, the X/Y offsets indicate how much the second image needs to be shifted by to match the first. This module does not perform warping or rotation, it simply shifts images in X and Y. For more complex registration tasks, you might preprocess images using a plugin for that purpose in FIJI/ImageJ.

| Supports 2D? | Supports 3D? | Respects masks? |
|--------------|--------------|-----------------| | Yes | No | Yes |

Measurements made by this module

• Xshift, Yshift: The pixel shift in X and Y of the aligned image with respect to the original image.

References

• Lewis JP. (1995) “Fast normalized cross-correlation.” Vision Interface, 1-7.

TrakEM2 is an ImageJ plugin for morphological data mining, three-dimensional modeling and image stitching, registration, editing and annotation (Fiji comes with TrakEM2). It supports arbitrary-sized datasets. 

This plugin registers (= aligns, matches) a stack of image slices.

From the plugin inline help:

" Align RGB planes v1.7 by G.Landini Changes the alignment of the RGB planes independently.

Red Green and Blue checkboxes switch ON and OFF the planes and undo the alignment since last plane change. Note that when switching planes, the portion of the previously edited plane left outside the image frame is lost. Rotation, Width and Height changes are interpolated (so there is some loss of sharpness) and do not retain the image portions outside the image frame. You can use the Resize2Rotate macro to avoid losing any image data.

The Rotate, Width and Height sliders set integer values, but fractional values can also be typed in the entry boxes. Just make sure you press [RETURN] after the number is typed.

The Revert button works only with single images, not stacks.

Note: When using stacks, 2 buttons [< Prev] and [Next >] are added to the panel. Do not use the slide bar in the stack window, but use those buttons instead."