Difference between revisions of "Slicer3:Registration"

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== Modules for Special Case Registration ==
 
== Modules for Special Case Registration ==
<gallery  widths="250px" heights="150px" perrow="3">
 
  
Image:Registration_ACPC_icon.png|The [[Modules:RealignVolume-Documentation-3.4|'''ACPC Transform''']] Module (Nicole Aucoin) is used to orient '''brain''' images along the anatomical reference line between the anterior and posterior commissure.
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{| cellpadding="2" cellspacing="2"
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|align="center" |[[Image:Registration_ACPC_icon.png|150px|link=Modules:RealignVolume-Documentation-3.4]]
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|align="center" |[[Image:Registration_Fiducial_icon.png|150px|link=Modules:FiducialRegistration]]
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|align="center" |[[Image:Registration_Surface_icon.png|150px|link=Modules:PythonSurfaceICPRegistration-Documentation-3.4]]
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|The [[Modules:RealignVolume-Documentation-3.4|'''ACPC Transform''']] Module (Nicole Aucoin) is used to orient '''brain''' images along the anatomical reference line between the anterior and posterior commissure.
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|The [[Modules:FiducialRegistration |'''Fiducial Alignment''']] Module (Casey Goodlett) can align images based on pairs of manually selected fiducial points (rigid and affine).
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|The [[Modules:PythonSurfaceICPRegistration-Documentation-3.4|'''ICP Surface Registration''' ]] Module (Luca Antiga) performs automated registration of surfaces (not images). This is useful if image data directly is unreliable, but surfaces can be produced from segmentations that provide good information about desired alignment.
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|-
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|align="center" |[[Image:Registration_Demons_icon.png|150px|link=Modules:DemonsRegistration-Documentation-3.5]]
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|align="center" |[[Image:Registration_HAMMER_icon.png|150px|link=Modules:RegisterImagesMultiRes-Documentation-3.6]]
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|The [[Modules:DemonsRegistration-Documentation-3.5|'''Demons Non-rigid Registration''' ]] Module performs automated registration of images based on an optic flow mechanism. Deformations here are significantly more "fluid" (i.e. have more DOF and are less constrained) than for the BSpline method.
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|The [http://na-mic.org/Wiki/index.php/2010_Winter_Project_Week_HAMMER '''HAMMER'''] Module (Guorong Wu, Dinggang Shen) performs elastic (non-rigid) alignment of '''brain''' images of different individuals based on tissue class segmentation and intensity (experimental stage).
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Image:Registration_Fiducial_icon.png|The [[Modules:FiducialRegistration |'''Fiducial Alignment''']] Module (Casey Goodlett) can align images based on pairs of manually selected fiducial points (rigid and affine).
 
 
Image:Registration_HAMMER_icon.png|The [http://na-mic.org/Wiki/index.php/2010_Winter_Project_Week_HAMMER '''HAMMER'''] Module (Guorong Wu, Dinggang Shen) performs elastic (non-rigid) alignment of '''brain''' images of different individuals based on tissue class segmentation and intensity (experimental stage).
 
 
Image:Registration_Demons_icon.png‎|The [[Modules:DemonsRegistration-Documentation-3.5|'''Demons Non-rigid Registration''' ]] Module performs automated registration of images based on an optic flow mechanism. Deformations here are significantly more "fluid" (i.e. have more DOF and are less constrained) than for the BSpline method.
 
Image:Registration_Surface_icon.png|The [[Modules:PythonSurfaceICPRegistration-Documentation-3.4|'''ICP Surface Registration''' ]] Module (Luca Antiga) performs automated registration of surfaces (not images). This is useful if image data directly is unreliable, but surfaces can be produced from segmentations that provide good information about desired alignment.
 
</gallery>
 
  
 
== Auxilary Modules for Registration ==
 
== Auxilary Modules for Registration ==

Revision as of 15:24, 12 April 2010

Home < Slicer3:Registration

Registration in 3D Slicer

An extensive set of tools is available within 3D Slicer to support your registration or image fusion task. The right module will depend on your input data and the underlying question asked. Below is an overview of the main and auxilary modules related to image registration. The spectrum ranges from fully automated to fiducial to fully interactive manual alignment, and from rigid to fully elastic image warping. Most modules are generic and can handle any image content, but a few are designed specifically for brain images. They have a brain contour in the icon.

There are also many auxilary/support modules that perform important functions you may need to successfully complete your registration, such as the ROI or Interactive Editor modules to obtain masks, or the Resample modules to properly apply your result transform to the image.
This page is organized by methods. Alternatively the Slicer Registration Case Library is organized by data type, i.e. it provides example cases, complete with tutorials, for a variety of registration problems collected in the "real world". You may find a good starting point and helpful discussion in those examples. If you find something amiss, please let us know so we can amend (meier at bwh.harvard.edu).

Slicer Registration Case Library: Call for Example Datasets

Consider adding your case to the library: We can (for 2010-2011) offer you direct consulting on your image registration problem: If we can add your (anonymized) case to the Library, we will try to register it for you, build a step-by-step tutorial and send you the registration strategy and solution parameters. See here for details.

Speed vs. Precision

Registration Speed icon.png Registration Precision icon.png
See here if registration speed is mission-critical. See here if precision is more important than speed.


Default Registration Modules

Registration Rigid+Affine icon.png Registration Manual icon.png Registration NonRigid icon.png
The Register Images Module performs automated image registration, rigid to affine, based on image intensity similarities. It allows to focus the registration on a region of interest Manual/interactive alignment can be done via the Transforms module, e.g. for initial alignment. [[Slicer3.4:Training#Slicer_3.4_Tutorials| Tutorial and Example Dataset on Manual Registration (PowerPoint) The Deformable B-Spline Registration Module performs non-rigid automated image registration.

Alternative Registration Modules

Registration Rigid icon.png Registration Affine icon.png Registration Multires icon.png
The Linear Registration Module performs automated rigid registration. This is being replaced by the Register Images Module that performs the same function. The Affine Registration Module performs automated affine registration. This is being replaced by the Register Images Module that performs the same function. The Multires Registration module performs robust automated affine image registration employing a multi-resolution scheme.

Modules for Special Case Registration

Registration ACPC icon.png Registration Fiducial icon.png Registration Surface icon.png
The ACPC Transform Module (Nicole Aucoin) is used to orient brain images along the anatomical reference line between the anterior and posterior commissure. The Fiducial Alignment Module (Casey Goodlett) can align images based on pairs of manually selected fiducial points (rigid and affine). The ICP Surface Registration Module (Luca Antiga) performs automated registration of surfaces (not images). This is useful if image data directly is unreliable, but surfaces can be produced from segmentations that provide good information about desired alignment.
Registration Demons icon.png Registration HAMMER icon.png
The Demons Non-rigid Registration Module performs automated registration of images based on an optic flow mechanism. Deformations here are significantly more "fluid" (i.e. have more DOF and are less constrained) than for the BSpline method. The HAMMER Module (Guorong Wu, Dinggang Shen) performs elastic (non-rigid) alignment of brain images of different individuals based on tissue class segmentation and intensity (experimental stage).


Auxilary Modules for Registration

Registration Examples / Use-Cases

The Slicer Registration Case Library contains a (growing) collection of registration example cases to download and try yourself, complete with step-by step tutorial, image data, parameter presets, solutions and discussion of the particular challenges and strategies. These are all real-life cases contributed by fellow researchers; they span a wide spectrum of anatomy and image modality, and consequently also present a wide range of registration solutions, from automated rigid brain alignment to surface-based knee registration. We hope you will find a case similar to yours in this library that will provide an educated starting point. If you cannot find a similar case, take advantage of our Call for Example Datasets to add your case to the library.
The Slicer Registration Case Library

Registration Work in Progress