deepPatchMatchMultiStart | R Documentation |
High-level function for deep patch matching that uses multiple starting points to reduce sensitivity to initialization.
deepPatchMatchMultiStart( movingImage, fixedImage, movingImageMask, fixedImageMask, movingPatchSize = 16, fixedPatchSize = 16, block_name = 20, vggmodel, subtractor = 127.5, patchVarEx = 0.95, meanCenter = FALSE, numberOfStarts = 1, sdAffine = 5, transformType = "Rigid", verbose = FALSE )
movingImage |
input image from which we extract patches that are transformed to the space of the fixed image |
fixedImage |
input image that provides the fixed reference domain. |
movingImageMask |
defines the object of interest in the movingImage |
fixedImageMask |
defines the object of interest in the fixedImage |
movingPatchSize |
integer greater than or equal to 32. |
fixedPatchSize |
integer greater than or equal to 32. |
block_name |
name of vgg feature block, either block2_conv2 or integer. use the former for smaller patch sizes. |
vggmodel |
prebuilt feature model |
subtractor |
value to subtract when scaling image intensity; should be chosen to match training paradigm eg 127.5 for vgg and 0.5 for resnet like. |
patchVarEx |
patch variance explained for ripmmarc |
meanCenter |
boolean to mean center each patch for ripmmarc |
numberOfStarts |
the number of starting points to try |
sdAffine |
standard deviation parameter e.g. 0.15 |
transformType |
one of Rigid, Affine and ScaleShear |
verbose |
boolean |
matched points and transformation
Avants BB
## Not run: library( tensorflow ) library( ANTsR ) nP1 = 250 nP2 = 500 psz = 12 img <- ri( 1 ) %>% iMath( "Normalize" ) img2 <- ri( 2 ) %>% iMath( "Normalize" ) mask = randomMask( getMask( img ), nP1 ) mask2 = randomMask( getMask( img2 ), nP2 ) match = deepPatchMatchMultiStart( img2, img, mask, mask2, numberOfStarts=3, verbose=FALSE ) ## End(Not run)
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