MediaWiki API result

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            "*": "Subscribe to the mediawiki-api-announce mailing list at <https://lists.wikimedia.org/mailman/listinfo/mediawiki-api-announce> for notice of API deprecations and breaking changes."
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                "title": "Registration:Categories",
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                        "*": "[[Slicer3:Registration|Back to registration portal page]]\n\n[http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseInventory '''Slicer Registration Case Library''': Examples & Tutorials] \n\n= <small><small>looking for a set of features:</small></small> 3DSlicer Registration Feature/Decision Matrix [[Image:Registration_DecisionMatrix.png|70px]] =\n*the matrix below shows which modules support which feature, intended to help you if looking for a method with a particular combination of features. \n*if you would like to see additional criteria or a new module is not yet listed, please let us know: mailto:slicer-users at bwh.harvard.edu\n*see here for an [http://labelpage.halle.us/static/reg/index.html '''interactive method selection tool''']\n[[Image:Registration_DecisionMatrix.png|left|850px|3DSlicer Registration Feature/Decision Matrix]]\n\n= <small><small>looking for</small></small>  Speed [[Image:Registration_Speed_icon.png|70px]] =\n\n*'''Manual'''/interactive alignment can be done via the [[Modules:Transforms-Documentation-3.6|'''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]]\n*'''Affine:''' The [[Modules:AffineRegistration-Documentation-3.6|'''Fast Affine Registration''']] module performs automated affine registration. \n*'''Bspline:''' The [[Modules:DeformableB-SplineRegistration-Documentation-3.6|'''Fast Nonrigid BSpline Registration''']] module performs non-rigid automated image registration. With default settings results within <1 minute are common.\n*The [[Modules:ACPCTransform-Documentation-3.6|'''AC-PC Transform''']] module is used to orient '''brain''' images along predefined anatomical landmarks: (manually defined)  fiducials for the inter-hemispheral midline. Once landmarks are defined, realignment is obtained instantly.\n\n= <small><small>looking for</small></small>  Robustness and/or Precision [[Image:Registration_Precision_icon.png| 70px]]  =\n*The [[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']] module performs automated image registration, rigid to affine, based on image intensity similarities. It allows to focus the registration on a region of interest\n*The [[Modules:RegisterImagesMultiRes-Documentation-3.6|'''Robust Multiresolution Affine Registration''']] module performs robust  automated affine image registration employing a multi-resolution scheme.\n*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.\n*The [[Modules:ACPCTransform-Documentation-3.6|'''AC-PC Transform''']] module is used to orient '''brain''' images along the anterior- and posterior commissure and midline. Within the accuracy of the manually chosen anatomical landmarks this alignment tends to be very robust.\n\n= <small><small>choose by</small></small> DOF  [[Image:Registration_HLogo_DOF.png| 70px]]  =\n*'''rigid 6 DOF:'''\n**[[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']]. Set DOF to ''PipelineRigid''.\n*'''similarity 9 DOF:'''\n**The [[Modules:BRAINSFit| '''BAINSfit''']] module includes similarity transform; type ''ScaleVersor3D'' in the transform type field; provides masking support.\n**The [[Modules:PythonSurfaceICPRegistration-Documentation-3.6|'''Surface Registration''' ]] module supports automated registration of surfaces with rigid or similarity transforms.\n**The [[Modules:TransformFromFiducials-Documentation-3.6|'''Fiducial Alignment''']] module supports a similarity transform.  Two sets of fiducials (fiducial lists) are required, forming matching pairs to be aligned. See ''Fiducials'' module.\n*'''affine 12 DOF:''' \n**[[Modules:AffineRegistration-Documentation-3.6|'''Fast Affine Registration''']] \n**The [[Modules:RegisterImagesMultiRes-Documentation-3.6|'''Robust Multiresolution Affine Registration''']] module performs robust  affine (12 DOF) image registration employing a multi-resolution scheme.\n**[[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']]. Set DOF to ''PipelineAffine''.\n\n*'''non rigid 27- 100s DOF:''' \n** The [[Modules:DeformableB-SplineRegistration-Documentation-3.6|'''Fast Nonrigid BSpline Registration''']] module performs non-rigid automated image registration.\n**The [[Modules:BRAINSFit| '''BAINSfit''']] module includes a  registration based on a Bspline transform. Initially designed for but not limited to '''brain''' images. Also includes many options such as masking support.\n*'''non rigid (fluid) >100 DOF''' \n**The [[Modules:BRAINSDemonWarp|'''BRAINSDemonWarp''' ]] module performs automated registration of brain MRI based on an optic flow mechanism. Deformations here are significantly more \"fluid\" (i.e. have more DOF and are less constrained) than for the nonrigid BSpline method. \n**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.\n**The [[Modules:HammerRegistration| '''HAMMER''']] Module performs elastic (non-rigid) alignment of '''brain''' images of different individuals based on tissue class segmentation and intensity (experimental stage).\n\n= <small><small>choose by</small></small> Datatype [[Image:Registration_HLogo_Datatype.png| 70px]]  =\n*'''images, same modality:'''  The [[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']] module performs automated image registration, rigid to affine, based on image intensity similarities. It allows to focus the registration on a region of interest\n*'''images, different modality:''' The [[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']] module performs automated image registration, rigid to affine, based on image intensity similarities. Select Mutual Information as cost function.\n*'''surfaces:''' The [[Modules:PythonSurfaceICPRegistration-Documentation-3.6|'''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.\n*'''fiducials:''' The [[Modules:TransformFromFiducials-Documentation-3.6|'''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. Use the [[Modules:Fiducials-Documentation-3.6|'''Fiducials''' module]] to create fiducial lists.   The [[Modules:ACPCTransform-Documentation-3.6|'''AC-PC Transform''']] module is also a special form of fiducial registration, used to orient '''brain''' images along the anterior- and posterior commissure and midline.\n\n= <small><small>looking for</small></small> Brain Image Registration [[Image:Registration_HLogo_Brain.png| 70px]]  =\n*The [[Modules:ACPCTransform-Documentation-3.6|'''AC-PC 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.\n**The [[Modules:BRAINSFit| '''BAINSfit''']] module includes a  registration based on a Bspline transform. Initially designed for but not limited to '''brain''' images. Also includes many options such as masking support.\n*The [[Modules:BRAINSDemonWarp|'''BRAINSDemonWarp''' ]] module performs automated registration of brain MRI based on an optic flow mechanism. Deformations here are significantly more \"fluid\" (i.e. have more DOF and are less constrained) than for the nonrigid BSpline method. \n*The [[Modules:HammerRegistration| '''HAMMER''']] Module performs elastic (non-rigid) alignment of '''brain''' images of different individuals based on tissue class segmentation and intensity (experimental stage).\n\n\n= <small><small>looking for</small></small> Tools to Resample Data [[Image:Registration_Resample_icon.png| 70px]]  =\n*see the [[Registration:Resampling|'''Overview of Resampling Tools''']] for all available resampling methods, including tools to resample in place (e.g. change resolution or voxel anisotropy etc.)\n\n= <small><small>looking for</small></small> Tools for Preparing Data for Registration [[Image:Registration_Masking_icon.png| 70px]]  =\n*Intensity Normalization & Filtering\n:Intensity corrections are often the first processing step of choice. MRI bias field inhomogeneities can adversely affect registration accuracy and stability, as can large differences in the intensity ranges between the two images, or large amounts of noise\n:*[[Modules:MRIBiasFieldCorrection-Documentation-3.6|Bias Field Correction to remove intensity drift across the image (e.g from variable coil sensitivity)]]\n:*[[Modules:HistogramMatching-Documentation-3.6|Histogram Equalization for matching intensity ranges]]\n:*[[Modules:MedianFilter-Documentation-3.6|Median Filtering to remove speckle noise without smoothing edges]]\n:*[[Modules:GaussianBlur-Documentation-3.6|Gaussian Blur]] filter to remove noise and smooth edges\n:*[[Documentation-3.6#Filtering|other filters see here]] \n*Masking\n:Masks are an important component of many registration tasks. They allow to focus the algorithm on the region of interest (ROI) that is to be registered and prevent it from being distracted by  image content outside this ROI.\n:* [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.\n:*[[Modules:Editor-Documentation-3.6|Thresholding within the Interactive Editor]] or via the [[Modules:ThresholdImage-Documentation-3.6|Threshold module]].\n:*[[Modules:OtsuThresholdSegmentation-Documentation-3.6|Otsu Threshold Segmentation]] for automated segmentation of main image object from background\n:*[[Modules:FastMarchingSegmentation-Documentation-3.6|Fast Marching segmentation]] for interactive fiducial-based segmentation of small local regions.\n:*[[Modules:MaskImage-Documentation-3.6|Mask image module]] to apply a mask and remove image content outside (if mask is not directly supported by the chosen registration method)\n:*[[Modules:ExtractSubvolumeROI-Documentation-3.6|Crop/SubvolumeExtraction module]] to extract a box region from the image.\n:*[[Modules:ROIModule-Documentation-3.6|ROI module]] to quickly define a box region of interest that is supported as mask by [[Modules:RegisterImagesMultiRes-Documentation-3.6|Robust Multires Registration]], for example."
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            "4599": {
                "pageid": 4599,
                "ns": 0,
                "title": "Registration:Resampling",
                "revisions": [
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                        "contentmodel": "wikitext",
                        "*": "*[[Slicer3:Registration|back to Slicer Registration Portal Page]]\n*[[Documentation-3.6|back to Slicer Documentation]]\n*[http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseInventory Back To Slicer Registration Case Library]\n\n\n= Resampling in 3D Slicer =\n{| style=\"color:#000000; background-color:#aaaaaa;\" cellpadding=\"10\" cellspacing=\"0\" border=\"0\"\n|Several distinct tools are available within 3D Slicer for '''resampling''' image data to change orientation, resolution or field of view. The organization below is intended to help you choose the best module for your task<br>\nNote that there are also many related functions that change aspects of the image without requiring a resampling, e.g. changing the aspect ratio or slice view orientation. They are listed in a separate category below. <br>\nIf you find something amiss, please let us know so we can amend (meier at bwh.harvard.edu). \n|\n|[[Image:SlicerRegistrationLibrary_Ad.png|left|400px|link=http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseAdvertisement|Slicer Registration Case Library: Call for Example Datasets]] <br>\n[http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseAdvertisement '''Consider adding your case to the library:''']  \nIf your resampling task is related to a registration problem, we can (for 2010-2011) offer you direct consulting: If we can add your (anonymized) case to the Library, we will assist you with registering/resampling by processing your case and providing step-by-step instructions and best-practice tips. [http://na-mic.org/Wiki/index.php/Projects:RegistrationDocumentation:UseCaseAdvertisement See here for details.] \n|}\n\n= What we mean by ''Resampling'' =\n'''Resampling''' builds a new dataset (image, surface, fiducials etc.) from an existing one, but with a different orientation, resolution, field of view or aspect ratio. For example the last step in registering two images consists of two main steps: finding the transform and resampling according to this transform. So the last step in registration will be to resample the moving data according to a spatial transform function, and thereby generate a new and aligned image. Or changing the voxel size to something larger or smaller involves resampling. \n[http://en.wikipedia.org/wiki/Resampling  See Wikipedia for a more detailed definition].<br>\n'''Interpolation''' is the process of estimating the value of the data based on surrounding values. This is necessary because spatial realignment is unlikely to be in exact multiples of voxel sizes.  Please pay attention to selecting the proper interpolation method for your data-type. For more detail on ''interpolation'' we recommend this [http://en.wikipedia.org/wiki/Interpolation Wikipedia article].\n----\n[[Image:Resampling DecisionMatrix.png|800px|left|Resampling Decision Matrix]]\n<br>\n''Resampling decision matrix, use to find the appropriate resampling tool given your input-data and desired functionality. Note that some modules listed, although performing functions related to resampling, do '''not''' produce an output volume.\n<br>\n\n= Resampling in Place: Change Resolution or Field of View=\n*The [[Modules:CropVolume-Documentation-3.6|'''Crop Volume''']] module lets you clip away the image data outside an interactively defined 3D box. Such ''cropping'' is very useful to focus automated processing (e.g. registration,  segmentation) onto a region of interest or reduce memory usage and speed up performance.  The cropping includes a resampling that allows to change the resolution (''spacing'') of the result image at the same time.\n*The [[Modules:ResampleVolume-Documentation-3.6|'''Resample Scalar Volume''']] module changes resolution (''spacing'') of an image, allowing several interpolation options for different data types.  This is the method of choice if you wish to increase or decrease the number of voxels per mm or make the voxel size isotropic. You need to specify the desired voxel size in mm. If you do not know the current voxel size of your image, go to the ''Info'' tab in the  [[Modules:Volumes-Documentation-3.6|Volumes]] module.   \n*The [[Modules:ResampleVolumeBatch-Documentation-3.6|'''Resample Scalar Volume Batch Make''']] module is the ''batch'' version of the above ''Resample Scalar Volume'' module. Use this if you have many image files that need to have their resolution changed/adjusted. The new resampled images will be written back out to a specified directory.\n*The [[Modules:CastImage-Documentation-3.6|'''Cast Image''']] module lets you change the underlying '''datatype''' of your image, e.g. change from ''floating point'' to an ''integer'' or vice versa. This will not perform an interpolation, since the number of image voxels stays the same, but may be necessary to prepare the image for some modules or to reduce the image (file) size. For example an image stored as ''float'' will need twice as much disk space than one stored as ''short'' and four times as much as a ''char''. \n*The [[Modules:DiffusionTensorEstimation-Documentation-3.6|'''Diffusion Tensor Estimation''']] module will produce a new tensor image from raw diffusion (DWI) MRI. The new volume will be in the same orientation and resolution as the input DWI.  There are three estimation methods available: least squares, weigthed least squares and non-linear estimation.\n*The [[Modules:Model_Into_Label_Volume_Documentation-3.6|'''Model Into Label Volume''']] module will do the inverse of the Model Maker and generate a binary 3D labelmap image with non-zero values at the location of the surface. This will let you bring the information from surface models back into an ''implicit'' image format. You set the resampling options, i.e. the resolution of the result via a reference image. Note that it will not fill a closed model, but produce points at the surface only.\n*The [[Modules:OrientImages-Documentation-3.6|'''Orient Images''']] module will sample the image data along a specified orientation scheme, such as axial/sagittal/coronal planes or RAS or LPI etc. Note that  the slices are '''not''' interpolated, they are just reordered and/or permuted. The resulting volume will cover the original volume.  Because the display is in physical space, you will not see an effect of this resampling in the viewer. \n*The [[Modules:Volumes-Documentation-3.6|'''Volumes''']] module lets you adjust the physical voxel size of an image (Info Tab), but will not generate a new volume. The changes will take effect immediately and you should see adjustments in your slice views. To obtain a new resampled volume, use the  [[Modules:ResampleVolume-Documentation-3.6|Resample Scalar Volume]] module described above.\n*The [[Modules:BRAINSResample|'''BRAINSresample''']] module lets you resample your image to a new resolution (specified implicitly via a reference image), with choice of interpolation method and output datatype.\n\n\n= Resampling via a spatial transform =\n*The [[Modules:ResampleScalarVectorDWIVolume-Documentation-3.6|'''Resample Scalar/Vector/DWI Volume''']] Module sends both scalar and vector images through a transform. Several interpolation options.\n*The ''Harden Transforms'' function (context menu via the right mouse button) in the [[Modules:Data-Documentation-3.6|'''Data''' Module]] can also be used to resample an image or fiducial set through a '''linear''' transform.\n*The [[Modules:ACPCTransform-Documentation-3.6|'''AC-PC Transform''']] module lets you realign your brain image along the anterior-posterior commissure and interhemispheral midline. You (manually) select fiducials to define these points. An output transform is generated that you can then apply to the image using the resampling tools described here.\n*The main registration modules ( [[Modules:AffineRegistration-Documentation-3.6|Affine Registration]],  [[Modules:RegisterImages-Documentation-3.6|Expert Automated Registration]],  [[Modules:DeformableB-SplineRegistration-Documentation-3.6|Deformable B-Spline Registration]],  [[Modules:RegisterImagesMultiRes-Documentation-3.6|Multires Affine Registration]], [[Modules:PythonSurfaceICPRegistration-Documentation-3.6|Surface Registration]], [[  Modules:TransformFromFiducials-Documentation-3.6|Fiducial Alignment]]  etc. modules all contain a resampling option, i.e. they offer to produce a direct result volume, which includes a resampling step. In most cases the interpolation is linear. If you wish for more control over how the resampling is performed (e.g. select a different interpolator or output size or voxel size), select the registration module's option to generate an output/saved transform and then use one of the dedicated resampling modules described here to generate the new volume. The exception are modules which do '''not''' (yet) offer a transform output (e.g. Fast Nonrigid BSpline); for those you must use the module-specific resampling options to generate a result image.\n*The [[Modules:BRAINSResample|'''BRAINSresample''']] module supports applying linear transforms and deformation fields to an image, with choice of interpolation method and output datatype.\n*The [[Modules:RegisterImages-Documentation-3.6|'''Expert Automated Registration''']] module offers pure resampling from a given transform: select ''None'' for initialization and ''None'' for registration, specify the transform to apply under ''Load Transform'' and set the interpolation type under ''Advanced Registration Parameters''.\n\n= Resampling Vector- and Tensor-Data =\n*The [[Modules:ResampleScalarVectorDWIVolume-Documentation-3.6|'''Resample ResampleScalarVectorDWIVolume''']] Module is the method of choice to realign scalar, vector or diffusion weighted data along a given transform. It supports both linear and nonlinear transforms as well as deformation fields. Note that tensor data, such as DTI has a separate volume (below). \n*The [[Modules:ResampleDTIVolume-Documentation-3.6|'''Resample DTI Volume''']] module is designed specifically for reorienting diffusion tensor MRI data.  It supports both linear and nonlinear transforms as well as deformation fields. Note that simply sending each component of a DTI tensor through the transform separately would yield an '''incorrect''' result. This module will transform the vector/tensor data correctly.\n\n= Resampling Surface- and Model-Data =\n*The [[Modules:ModelTransform-Documentation-3.6|'''Model Transform''']] Module reorients your surface model based on a transform. It creates a new model which is a transformed version of the input polygonal model\n*The [[Modules:Model Into Label Volume Documentation-3.6|'''Modules:Model Into Label Volume''']] module will resample a model back into a labelmap (outline, non-filled).\n*The [[Modules:PolyDataToLabelmap-Documentation-3.6|'''PolyData To Labelmap''']] module will resample a model back into a labelmap while filling the interior."
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