Difference between revisions of "Slicer3:Registration"

Revision as of 21:41, 19 January 2010

Home < Slicer3:Registration

The Register Images Module (Casey Goodlett, Stephen Aylward) performs automated image registration, rigid to affine, based on image intensity similarities.

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 Deformable B-Spline Registration Module (Bill Lorensen) performs automated image warping based on image intensities.

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 Transforms 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.

Transformation matrices derived from the above modules can be used as input for resampling other volumes (including DTI) using the Resample Volume 2 module.
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.

@@ Line 24: / Line 24: @@
 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) can align brain images of different individuals based on tissue class segmentation and intensity (experimental stage).
+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_Manual_icon.png|The [[Modules:PythonSurfaceICPRegistration-Documentation-3.4|'''Transforms''' ]] 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.