Difference between revisions of "Slicer3:Registration"

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== Registration in 3D Slicer ==
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= Registration in 3D Slicer =
 
{| style="color:#000000; background-color:#aaaaaa;" cellpadding="10" cellspacing="0" border="0"
 
{| style="color:#000000; background-color:#aaaaaa;" cellpadding="10" cellspacing="0" border="0"
 
|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.<br>
 
|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.<br>
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== Speed vs. Precision ==
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= Speed vs. Precision =
 
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|align="center" |[[Image:Registration_Speed_icon.png|135px|link=Registration:Speed]]
 
|align="center" |[[Image:Registration_Speed_icon.png|135px|link=Registration:Speed]]
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= Default Registration Modules=
== Default Registration Modules==
 
  
 
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== Alternative  Registration Modules ==
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= Alternative  Registration Modules =
  
 
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== Modules for Special Case Registration ==
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= Modules for Special Case Registration =
  
 
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|align="center" |[[Image:Registration_Surface_icon.png|135px|link=Modules:PythonSurfaceICPRegistration-Documentation-3.4]]
 
|align="center" |[[Image:Registration_Surface_icon.png|135px|link=Modules:PythonSurfaceICPRegistration-Documentation-3.4]]
 
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|The [[Modules:RealignVolume-Documentation-3.4|'''ACPC Transform''']] Module is used to orient '''brain''' images along the anatomical reference line between the anterior and posterior commissure.
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|The [[Modules:RealignVolume-Documentation-3.4|'''ACPC Transform''']] Module is used to orient '''brain''' images along predefined anatomical landmarks: (manually defined)  fiducials for the inter-hemispheral midline, anterior- and posterior commissure are used to align an image such that these landmarks become vertical and horizontal, respectively.
|The [[Modules:FiducialRegistration |'''Fiducial Alignment''']] Module can align images based on pairs of manually selected fiducial points (rigid and affine).
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|The [[Modules:FiducialRegistration |'''Fiducial Alignment''']] Module can align images based on pairs of manually selected fiducial points (rigid and affine). Two sets of fiducials (fiducial lists) are required, forming matching pairs to be aligned. See ''Fiducials'' module below.
|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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|The [[Modules:PythonSurfaceICPRegistration-Documentation-3.4|'''ICP Surface Registration''' ]] Module 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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|align="center" |[[Image:Registration_Demons_icon.png|135px|link=Modules:DemonsRegistration-Documentation-3.5]]
 
|align="center" |[[Image:Registration_Demons_icon.png|135px|link=Modules:DemonsRegistration-Documentation-3.5]]
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= Auxilary Modules for Registration =
== Auxilary Modules for Registration ==
 
  
 
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== Registration Examples / Use-Cases ==
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= Registration Examples / Use-Cases =
 
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|The [http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseInventory '''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 [http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseAdvertisement Call for Example Datasets] to add your case to the library.
 
|The [http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseInventory '''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 [http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseAdvertisement Call for Example Datasets] to add your case to the library.
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== Registration Work in Progress ==
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= Registration Work in Progress =
 
*[http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation Registration Enhancement: Documentation & User Support]
 
*[http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation Registration Enhancement: Documentation & User Support]
 
*[http://na-mic.org/Wiki/index.php/Projects:RegistrationImprovement Registration Enhancement: New Algorithms & GUI Revisions]
 
*[http://na-mic.org/Wiki/index.php/Projects:RegistrationImprovement Registration Enhancement: New Algorithms & GUI Revisions]
 
*[[Slicer3:RegistrationLinks|Links to old/obsolete Registration pages (Slicer & NA-MIC)]]
 
*[[Slicer3:RegistrationLinks|Links to old/obsolete Registration pages (Slicer & NA-MIC)]]

Revision as of 16:11, 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. See 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 is used to orient brain images along predefined anatomical landmarks: (manually defined) fiducials for the inter-hemispheral midline, anterior- and posterior commissure are used to align an image such that these landmarks become vertical and horizontal, respectively. The Fiducial Alignment Module can align images based on pairs of manually selected fiducial points (rigid and affine). Two sets of fiducials (fiducial lists) are required, forming matching pairs to be aligned. See Fiducials module below. The ICP Surface Registration Module 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 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 ROI icon.png SubvolumeExraction icon.png Registration Fiducials.png
The ROI Volume can be used to define a local box region to be considered exclusively for automated registration. The ROISubvolume Extraction module can be used to extract a box region as a new volume and thus focus registration on a region of interest. The Fiducials Module is used to place fiducial pairs that can be used to run Fiducial-based registration or to evaluate registration quality
Registration DataModule.png Registration EDitor icon.png Skull Stripping
The Data Module is used to apply transforms on the fly to one or more volumes, to resample and concatenate transforms. Interaction is by drag & drop of nodes in the tree and via a right-mouse click context menu, e.g. to apply a transform. The Interactive Editor can be used to draw/define ROI regions that can be used as mask input to the automated registration. Skull Stripping] Extension Module automatically builds a mask of the brain from an input MRI image (T1w is best). This is an extension module and needs to be installed via the Extension manager.
Registration OtsuThreshold icon.png MaskImage Module icon.png Registration DTIresample icon.png
The Otsu's Segmentation Module can also be used to automatically generate a registration ROI/mask by identifying your main image object from the background. For more controlled mask building use the threshold and editing functions in the Interactive Editor. The Mask Image Module can be used to apply a mask and create a new volume with all unwanted structure removed. Use this approach if your registration method of choice does not (yet) support direct masking as part of the input parameters. The DTI Resample Module is used to apply a given transform to the DTI tensor data.
Registration Resample icon.png Registration Resample icon.png Registration Subtraction icon.png
The Resample Volume Module can be used to apply a given transform to a volume, with specific interpolation settings (linear, nearest neighbor and five flavors of sinc). The Resample Volume2 Module (Francois Budin) implements image and vector-image resampling through the use of ITK Transforms (rigid, affine, BSpline). The Subtract Images Module can be used to evaluate registration quality, particularly of intra-subject intra-modality cases.
link=http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter
The Checkerboard Filter can be used to evaluate registration quality

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