Difference between revisions of "Reg average"
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==Usage== | ==Usage== | ||
<code><pre> | <code><pre> | ||
+ | * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | ||
+ | usage: | ||
+ | reg_average <outputFileName> [OPTIONS] | ||
+ | -avg <inputAffineName1> <inputAffineName2> ... <inputAffineNameN> | ||
+ | If the input are images, the intensities are averaged | ||
+ | If the input are affine matrices, out=expm((logm(M1)+logm(M2)+...+logm(MN))/N) | ||
+ | |||
+ | -avg_lts <AffineMat1> <AffineMat2> ... <AffineMatN> | ||
+ | It will estimate the robust average affine matrix by considering 503000uliers. | ||
+ | |||
+ | -avg_tran <referenceImage> <transformationFileName1> <floatingImage1> ... <transformationFileNameN> <floatingImageN> | ||
+ | All input images are resampled into the space of <reference image> and averaged | ||
+ | A cubic spline interpolation scheme is used for resampling | ||
+ | |||
+ | -demean1 <referenceImage> <AffineMat1> <floatingImage1> ... <AffineMatN> <floatingImageN> | ||
+ | The demean1 option enforces the mean of all affine matrices to have | ||
+ | a Jacobian determinant equal to one. This is done by computing the | ||
+ | average transformation by considering only the scaling and shearing | ||
+ | arguments.The inverse of this computed average matrix is then removed | ||
+ | to all input affine matrix beforeresampling all floating images to the | ||
+ | user-defined reference space | ||
+ | |||
+ | -demean2 <referenceImage> <NonRigidTrans1> <floatingImage1> ... <NonRigidTransN> <floatingImageN> | ||
+ | -demean3 <referenceImage> <AffineMat1> <NonRigidTrans1> <floatingImage1> ... <AffineMatN> <NonRigidTransN> <floatingImageN> | ||
+ | |||
+ | |||
+ | --version Print current source code git hash key and exit | ||
+ | (3d24c3580a0cd227f30540578b3f84eca9d01e4a) | ||
+ | * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | ||
</pre></code> | </pre></code> |
Latest revision as of 11:35, 3 October 2014
Usage
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usage:
reg_average <outputFileName> [OPTIONS]
-avg <inputAffineName1> <inputAffineName2> ... <inputAffineNameN>
If the input are images, the intensities are averaged
If the input are affine matrices, out=expm((logm(M1)+logm(M2)+...+logm(MN))/N)
-avg_lts <AffineMat1> <AffineMat2> ... <AffineMatN>
It will estimate the robust average affine matrix by considering 503000uliers.
-avg_tran <referenceImage> <transformationFileName1> <floatingImage1> ... <transformationFileNameN> <floatingImageN>
All input images are resampled into the space of <reference image> and averaged
A cubic spline interpolation scheme is used for resampling
-demean1 <referenceImage> <AffineMat1> <floatingImage1> ... <AffineMatN> <floatingImageN>
The demean1 option enforces the mean of all affine matrices to have
a Jacobian determinant equal to one. This is done by computing the
average transformation by considering only the scaling and shearing
arguments.The inverse of this computed average matrix is then removed
to all input affine matrix beforeresampling all floating images to the
user-defined reference space
-demean2 <referenceImage> <NonRigidTrans1> <floatingImage1> ... <NonRigidTransN> <floatingImageN>
-demean3 <referenceImage> <AffineMat1> <NonRigidTrans1> <floatingImage1> ... <AffineMatN> <NonRigidTransN> <floatingImageN>
--version Print current source code git hash key and exit
(3d24c3580a0cd227f30540578b3f84eca9d01e4a)
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