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| | |align="center" |[[Image:Registration_NonRigid_icon.png| 135px |link=Modules:DeformableB-SplineRegistration-Documentation-3.4]] | | |align="center" |[[Image:Registration_NonRigid_icon.png| 135px |link=Modules:DeformableB-SplineRegistration-Documentation-3.4]] |
| | |- | | |- |
| − | |The [[Modules:RegisterImages-Documentation-3.4|'''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 | + | |The [[Modules:ResampleVolume-Documentation-3.6|'''Resample Scalar Volume''']] Module changes resolution (''spacing'') of an image. Several interpolation options. |
| | |Manual/interactive alignment can be done via the [[Modules:Transforms-Documentation-3.4|'''Transforms''' ]] module, e.g. for initial alignment. See [[Slicer3.4:Training#Slicer_3.4_Tutorials| here for a tutorial and example dataset on Manual Registration]] | | |Manual/interactive alignment can be done via the [[Modules:Transforms-Documentation-3.4|'''Transforms''' ]] module, e.g. for initial alignment. See [[Slicer3.4:Training#Slicer_3.4_Tutorials| here for a tutorial and example dataset on Manual Registration]] |
| | |The [[Modules:DeformableB-SplineRegistration-Documentation-3.4|'''Deformable B-Spline Registration''']] Module performs non-rigid automated image registration. | | |The [[Modules:DeformableB-SplineRegistration-Documentation-3.4|'''Deformable B-Spline Registration''']] Module performs non-rigid automated image registration. |
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| | = Resampling via a spatial transform = | | = Resampling via a spatial transform = |
| − | | + | *The [[Modules:ResampleScalarVectorDWIVolume-Documentation-3.6|'''Resample ResampleScalarVectorDWIVolume''']] Module sends both scalar and vector images through a transform. Several interpolation options. |
| − | {| cellpadding="6" cellspacing="2"
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| − | |align="center" |[[Image:Registration_Rigid_icon.png| 135px |link=Modules:LinearRegistration-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_Affine_icon.png| 135px |link=Modules:AffineRegistration-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_Multires_icon.png| 135px |link=Modules:RegisterImagesMultiRes-Documentation-3.6]]
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| − | |-
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| − | |The [[Modules:LinearRegistration-Documentation-3.4|'''Linear Registration''']] Module performs automated rigid registration. This is being replaced by the [[Modules:RegisterImages-Documentation-3.4|Register Images]] Module that performs the same function.
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| − | |The [[Modules:AffineRegistration-Documentation-3.4|'''Affine Registration''']] Module performs automated affine registration. This is being replaced by the [[Modules:RegisterImages-Documentation-3.4|Register Images]] Module that performs the same function.
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| − | |The [[Modules:RegisterImagesMultiRes-Documentation-3.6|'''Multires Registration''']] module performs robust automated affine image registration employing a multi-resolution scheme.
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| − | |}
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| | | | |
| | = Resampling Vector- and Tensor-Data = | | = Resampling Vector- and Tensor-Data = |
| | | | |
| − | {| cellpadding="6" cellspacing="2"
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| − | |align="center" |[[Image:Registration_ACPC_icon.png|135px|link=Modules:RealignVolume-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_Fiducial_icon.png|135px|link=Modules:FiducialRegistration]]
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| − | |align="center" |[[Image:Registration_Surface_icon.png|135px|link=Modules:PythonSurfaceICPRegistration-Documentation-3.4]]
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| − | |-
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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.
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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.
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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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| − | |-
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| − | |align="center" |[[Image:Registration_Demons_icon.png|135px|link=Modules:DemonsRegistration-Documentation-3.5]]
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| − | |align="center" |[[Image:Registration_HAMMER_icon.png|135px|link=http://na-mic.org/Wiki/index.php/2010_Winter_Project_Week_HAMMER ]]
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| − | |align="center" |[[Image:Plastimatch_icon.png|135px|link=Modules:Plastimatch]]
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| − | |-
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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 performs elastic (non-rigid) alignment of '''brain''' images of different individuals based on tissue class segmentation and intensity (experimental stage).
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| − | |The [[Modules:Plastimatch|'''Plastimatch''']] Module performs automated registration of images from rigid to affine to non-rigid. As a unique feature it provides non-rigid deformation from fiducials, which can be used to "edit/repair" a registration.
| |
| − | |}
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| | | | |
| | = Related Functions = | | = Related Functions = |
| − |
| |
| − | {| cellpadding="6" cellspacing="2"
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| − | |align="center" |[[Image:Registration_ROI_icon.png|100px|link=Modules:ROIModule-Documentation-3.4]]
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| − | |align="center" |[[Image:SubvolumeExraction_icon.png| 100px |link=Modules:ExtractSubvolumeROI-Documentation-3.5]]
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| − | |align="center" |[[Image:Registration_Fiducials.png| 100px |link=Modules:Fiducials-Documentation-3.4]]
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| − | |-
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| − | |The [[Modules:ROIModule-Documentation-3.4|ROI Volume]] can be used to define a local box region to be considered exclusively for automated registration. Registration modules that do not support ROIs directly, use the ExtractSubvolumeROI module on the right.
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| − | |The [[Modules:ExtractSubvolumeROI-Documentation-3.5|ROISubvolume Extraction]] module can be used to extract a box region as a new volume and thus focus registration on a region of interest. Use this if registration of entire FOV fails due to too much "distracting" image content.
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| − | |The [[Modules:Fiducials-Documentation-3.4|Fiducials Module]] is used to place fiducial pairs that can be used to run Fiducial-based registration (e.g. AC-PC alignment or FiducialRegistration above) or to evaluate registration quality.
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| − | |-
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| − | |align="center" |[[Image:Registration_DataModule.png| 100px |link=Modules:Data-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_EDitor_icon.png| 100px |link=Modules:Editor-Documentation-3.4]]
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| − | |align="center" |[[Image:SkullStripping_icon.png| 100px |link=http://www.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_Skull_Stripping|Skull Stripping]]
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| − | |
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| − | |-
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| − | |The [[Modules:Data-Documentation-3.4|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.
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| − | |The [[Modules:Editor-Documentation-3.4|Interactive Editor]] can be used to create or edit ROI regions that can be used as mask input to the automated registration. It contains interactive thresholding and other segmentation methods, and various forms of morphological processing.
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| − | |The [http://www.na-mic.org/Wiki/index.php/2009_Summer_Project_Week_Skull_Stripping 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.
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| − | |-
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| − | |align="center" |[[Image:Registration_OtsuThreshold_icon.png| 100px |link=Modules:OtsuThreshold-Documentation-3.4]]
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| − | |align="center" |[[Image:MaskImage_Module_icon.png| 100px |link=Modules:MaskImage-Documentation-3.5]]
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| − | |align="center" |[[Image:Registration_DTIresample_icon.png| 100px |link=Modules:ResampleDTIVolume-Documentation-3.4]]
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| − | |
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| − | |-
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| − | |The [[Modules:OtsuThreshold-Documentation-3.4|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.
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| − | |The [[Modules:MaskImage-Documentation-3.5|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.
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| − | |The [[Modules:ResampleDTIVolume-Documentation-3.4|DTI Resample]] Module is used to apply a given transform to the DTI tensor data. '''Do not''' resample vector or tensor data with routines meant for scalar data. A special resampling algorithm is required to properly align such multi-dimensional data.
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| − | |-
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| − | |align="center" |[[Image:Registration_Resample_icon.png| 100px |link=Modules:ResampleVolume-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_Resample_icon.png| 100px |link=Modules:ResampleVolume2-Documentation-3.4]]
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| − | |align="center" |[[Image:Registration_Subtraction_icon.png| 100px |link=Modules:SubtractImages-Documentation-3.4]]
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| − | |
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| − | |-
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| − | |The [[Modules:ResampleVolume-Documentation-3.4|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 [[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] Module (Francois Budin) implements image and vector-image resampling through the use of ITK Transforms (rigid, affine, BSpline).
| |
| − | |The [[Modules:SubtractImages-Documentation-3.4|Subtract Images]] Module can be used to evaluate registration quality, particularly of intra-subject intra-modality cases.
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| − | |-
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| − | |align="center" |[[Image:Registration_CheckerBoard_icon.png| 100px |link=http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter]]
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| − | |align="center" |[[Image:Registration_ModelXform_icon.png| 100px |link=Modules:ModelTransform-Documentation-3.6#Module_Name]]
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| − | |
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| − | |-
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| − | |The [http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter] can be used to evaluate registration quality. It generates an alternating pattern showing elements of both images. Useful to check non-rigid registration by evaluating the continuity of edges and other high-contrast features.
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| − | |The [[Modules:ModelTransform-Documentation-3.6#Module_Name|Model Transform]] module moves surface models according to an input transform
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| − | |
| |
| − | |}
| |
