Difference between revisions of "Slicer3:Registration"
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Image:Registration_Resample_icon.png|The [[[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] (Francois Budin) | Image:Registration_Resample_icon.png|The [[[Modules:ResampleVolume2-Documentation-3.4|Resample Volume2]] (Francois Budin) | ||
Image:Registration_Subtraction_icon.png|The [[Modules:SubtractImages-Documentation-3.4|Subtract Images]] can be used to evaluate registration quality, particularly of intra-subject intra-modality cases. | Image:Registration_Subtraction_icon.png|The [[Modules:SubtractImages-Documentation-3.4|Subtract Images]] can be used to evaluate registration quality, particularly of intra-subject intra-modality cases. | ||
| + | Image:Registration_CheckerBoard_icon.png|The [http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter] can be used to evaluate registration quality | ||
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*[[Modules:Editor-Documentation-3.4|Interactive Editor]] can be used to draw/define ROI regions that can be used as mask input to the automated registration. | *[[Modules:Editor-Documentation-3.4|Interactive Editor]] can be used to draw/define ROI regions that can be used as mask input to the automated registration. | ||
*[[Modules:OtsuThreshold-Documentation-3.4|Otsu's Segmentation Module]] is an automated thresholding technique that can also be used to quickly identify your object from the background and use the resulting label-map as mask in automated registration | *[[Modules:OtsuThreshold-Documentation-3.4|Otsu's Segmentation Module]] is an automated thresholding technique that can also be used to quickly identify your object from the background and use the resulting label-map as mask in automated registration | ||
| − | * | + | *[[Modules:ResampleDTIVolume-Documentation-3.4|DTI resample module]] is used to apply a given transform to the DTI tensor data. |
*[http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter] can be used to evaluate registration quality | *[http://www.slicer.org/slicerWiki/index.php/Modules:CheckerboardFilter-Documentation-3.4 Checkerboard Filter] can be used to evaluate registration quality | ||
Revision as of 22:54, 19 January 2010
Home < Slicer3:RegistrationContents
Default Registration Module
The Register Images Module (Casey Goodlett, Stephen Aylward) performs automated image registration, rigid to affine, based on image intensity similarities.
Alternative Registration Modules
The Transforms Module(Alex Yarmarkovich) allows to manually and interactively move one image to align with another (rigid only). This can be used for initial alignment.
The Linear Registration (Daniel Blezek) module performs automated rigid registration. This is being replaced by the Register Images Module that performs the same function.
The Affine Registration Daniel Blezek) module performs automated affine registration. This is being replaced by the Register Images Module that performs the same function.
The Deformable B-Spline Registration Module (Bill Lorensen) performs automated image warping based on image intensities.
Modules for Special Case Registration
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 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).
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.
Auxilary Modules
ROI Volume can be used to define a local box region to be considered only for automated registration..
The Fiducials Module is used to place fiducial pairs that can be used to run Fiducial-based registration or to evaluate registration quality
Data Module is used to apply transforms on the fly to one or more volumes, to resample and concatenate transforms.
The Interactive Editor can be used to draw/define ROI regions that can be used as mask input to the automated registration.
The Otsu's Segmentation Module is an automated thresholding technique that can also be used to quickly identify your object from the background and use the resulting label-map as mask in automated registration
The DTI resample module is used to apply a given transform to the DTI tensor data.
The Resample Volume can be used to apply a given transform to a volume, with specific interpolation settings.
Resample Volume2]] (Francois Budin)
The Subtract Images can be used to evaluate registration quality, particularly of intra-subject intra-modality cases.
The Checkerboard Filter can be used to evaluate registration quality
- ROI Volume can be used to define a local box region to be considered only for automated registration.
- Fiducials Module is used to place fiducial pairs that can be used to run Fiducial-based registration or to evaluate registration quality
- Data Module is used to apply transforms on the fly to one or more volumes, to resample and concatenate transforms.
- Interactive Editor can be used to draw/define ROI regions that can be used as mask input to the automated registration.
- Otsu's Segmentation Module is an automated thresholding technique that can also be used to quickly identify your object from the background and use the resulting label-map as mask in automated registration
- DTI resample module is used to apply a given transform to the DTI tensor data.
- Checkerboard Filter can be used to evaluate registration quality
- Resample Volume can be used to apply a given transform to a volume, with specific interpolation settings.
- Resample Volume2 (Francois Budin)
- Subtract Images can be used to evaluate registration quality, particularly of intra-subject intra-modality cases.
