ANTsR (version 0.4.0)

jointIntensityFusion: joint intensity fusion

Description

Estimates an image from another set of images - intensity generalization of joint label fusion. Search radius used only when employing labels. This program is for research. For studies, please use jointLabelFusion in ANTs or in ANTsR.

Usage

jointIntensityFusion(targetI, targetIMask, atlasList, beta = 4, rad = NA,
  doscale = TRUE, doNormalize = TRUE, maxAtlasAtVoxel = c(1, Inf),
  rho = 0.01, usecor = FALSE, boundary.condition = "image", rSearch = 2,
  jifImage = NA, imputeList = NA, imputedImage = NA)

Arguments

targetI

antsImage to be approximated

targetIMask

mask with value 1

atlasList

list containing antsImages

beta

weight sharpness, default to 2

rad

neighborhood radius, default to 3

doscale

scale neighborhood intensities

doNormalize

normalize each image range to 0, 1

maxAtlasAtVoxel

min/max n atlases to use at each voxel

rho

ridge penalty increases robustness to outliers but also makes image converge to average

usecor

employ correlation as local similarity

boundary.condition

one of 'image' 'mean' 'NA'

rSearch

radius of search, default is 2

jifImage

the current estimated jif image (helps speed slice by slice)

imputeList

2nd modality to impute

imputedImage

current estimate of 2nd modality

Value

approximated image, segmentation and probabilities

Examples

# NOT RUN {
set.seed(123)
ref<-antsImageRead( getANTsRData("r16"))
ref<-resampleImage(ref,c(50,50),1,0)
ref<-iMath(ref,"Normalize")
mi<-antsImageRead( getANTsRData("r27"))
mi2<-antsImageRead( getANTsRData("r30"))
mi3<-antsImageRead( getANTsRData("r62"))
mi4<-antsImageRead( getANTsRData("r64"))
mi5<-antsImageRead( getANTsRData("r85"))
refmask<-getMask(ref)
refmask<-iMath(refmask,"ME",2) # just to speed things up
ilist<-list(mi,mi2,mi3,mi4,mi5)
implist<-list()
for ( i in 1:length(ilist) )
 {
 ilist[[i]]<-iMath(ilist[[i]],"Normalize")
 mytx<-antsRegistration(fixed=ref , moving=ilist[[i]] ,
   typeofTransform = c("Affine") )
 mywarpedimage<-antsApplyTransforms(fixed=ref,moving=ilist[[i]],
   transformlist=mytx$fwdtransforms)
 ilist[[i]]=mywarpedimage
 implist[[i]]=mywarpedimage %>% iMath("Laplacian")
 }
r<-2
d<-2
pp<-jointIntensityFusion( ref, refmask, ilist, rSearch=1, rad=rep(r,d) )
pp<-jointIntensityFusion( ref, refmask, ilist, rSearch=1, rad=rep(r,d),
  imputeList=implist )

# }