inst/Tests/Test2-4_fctsGR.R
In bozenne/MRIaggr: Management, Display, and Processing of Medical Imaging Data
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%
#%%%%% Fichier test 2.4 : Test des fonctions GR
#%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
require(MRIaggr)
options(error=function() traceback(2))
options(max.print=10000)
#### 1- calc ####
#### >a calcCriteriaGR - private ####
# ?calcCriteriaGR
# MRIaggr:::calcCriteriaGR
# function(contrast,groups,W=NULL,sigma=NULL,breaks,
# criterion_d1=FALSE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE){
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
resGR_0.8 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=0.8,
iter_max=20)
resEvalGR_0.8 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_0.8$GR,W=W,sigma=0.8,breaks=resGR_0.8$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_0.8
resGR_1 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=20)
resEvalGR_1 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_1$GR,W=W,sigma=1,breaks=resGR_1$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_1
resGR_2 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=2,
iter_max=20)
resEvalGR_2 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_2$GR,W=W,sigma=2,breaks=resGR_2$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_2
#### example
#### >b calcGR ####
# ?calcGR
# calcGR
# function(contrast,W,seed,sigma_max,range=c(-Inf,+Inf),range.seed=c(-Inf,+Inf),coords=NULL,sd.iter=4,
# nbreaks=100,breaks=NULL,scale=FALSE,iter_max=100,sd.robust=FALSE,trace=TRUE,
# history_sigma=FALSE,history_step=FALSE,history_front=FALSE)
#### test
resGR0 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=20,
range=c(7,10))
# display
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR0$GR,],pch=20,cex=1),
index2=list(coords=coords[seed,],pch=20,cex=1)
)
#### test sphere
# n <- 100
# for(iter in c(10,20,30,40,50)){
# radius <- iter
# coords <- cbind(expand.grid(1:n,1:n),1)
# contrast <- as.numeric(sqrt((coords[,1]-n/2)^2+(coords[2]-n/2)^2)<radius)
# # W <- calcW(coords,range=sqrt(2),upper=NULL)
#
# # multiplot(coords,
# # contrast)
#
# resGR0 <- calcGR(contrast=contrast, W=W,
# seed=which( (coords[,1]==50)*(coords[,2]==50) == 1 ),
# sigma_max=10,range=c(0.5,+Inf),
# iter_max=50,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
#
# # multiplot(coords,contrast,
# # index1=coords[resGR0$GR,],main=iter)
#
# mean_step <- mean(resGR0$history_step)
# sd_step <- sd(resGR0$history_step)
#
# multiplot(coords[resGR0$GR,],(resGR0$history_step)/sd_step,
# main=iter)
# }
# hist(resGR0$history_step)
#
# IC_step <- mean_step + c(-1,1)*sd_step*qnorm(0.975)
#### example
## load an MRIaggr object
data(MRIaggr.Pat1_red, package="MRIaggr")
## display raw parameter
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,
breaks=seq(0,10,0.1),as.logical=TRUE,
index1=list(coords="MASK_DWI_t0",outline=TRUE))
## extract raw parameter, coordinates and compute the neighborhood matrix
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
## the seed is taken to be the point with the largest TTP in the lesion mask
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
## Display step by step the GR algorithm with sigma = 1
for(iter in c(0,1,2,5,10,15)){
resGR1 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=iter,trace=FALSE)
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR1$GR,],pch=20,cex=1),
index2=list(coords=coords[seed,],pch=20,cex=1)
)
}
## GR with sigma = 0.8
resGR2 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,sigma_max=0.8,iter_max=50,
history_step=TRUE,history_front=TRUE)
## display
# display the GR over the raw contrast
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR2$GR,],pch=20,cex=1)
)
# display the step of inclusion in GR group for each observation
multiplot(coords[resGR2$GR,],
resGR2$history_step,breaks=0:10,
index1=list(coords=coords[seed,]),
palette=rainbow(10)
)
# display the front propagation
multiplot(coords[resGR2$GR,],
resGR2$Mfront[,10],
index1=list(coords=coords[seed,])
)
## GR with sigma = 2
resGR3 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=2,
iter_max=20)
## display
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR3$GR,],pch=20,cex=1)
)
#### >c calcSigmaGR ####
# ?calcSigmaGR
# calcSigmaGR
# function(contrast,W,seed,sigma,range=c(-Inf,+Inf),range.seed=c(-Inf,+Inf),
# breaks=100,scale=FALSE,iter_max=100,sd.robust=FALSE,
# criterion_d1=FALSE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE,
# trace=TRUE,mar=rep(2,4),mgp=c(2,0.75,0),main="",
# window=FALSE,filename="calcSigmaGR",width=1000,height=700,path=NULL,unit="px",res=NA)
#### test
#### example
## load an MRIaggr object
data(MRIaggr.Pat1_red, package="MRIaggr")
## select data
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
## find optimal sigma
resGR_sigma <- calcSigmaGR(contrast=carto$TTP_t0, W=W,seed=seed,
sigma=seq(0.8,2,0.1),iter_max=50)
#### >d GRalgo - private ####
# ?GRalgo
# MRIaggr:::GRalgo
# function (contrast, W, seed, sigma_max, range, breaks,
# step, operator, iter_max, history)
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,
breaks=seq(0,10,0.1),as.logical=TRUE,
index1=list(coords="MASK_DWI_t0",outline=TRUE))
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0","T2_FLAIR_t2"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
# display
resGR0 <- MRIaggr:::GRalgo(contrast=carto$TTP_t0, W=W,
seed=indexN,sigma_max=1,range=c(7,+Inf),
breaks=seq(-5,40,1),step=1,operator="sd",
iter_max=10,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR0$GR,],pch=20,cex=1),
index2=list(coords=coords[indexN,],pch=20,cex=1)
)
multiplot(coords[resGR0$GR,],
as.numeric(unlist(lapply(strsplit(resGR0$history_front,split=".",fixed=TRUE),"[",1)))
)
#### T2
carto <- selectContrast(MRIaggr.Pat1_red,param=c("TTP_t0","MASK_DWI_t0","T2_FLAIR_t2"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
multiplot(MRIaggr.Pat1_red,param="T2_FLAIR_t2",hemisphere="lesion")
system.time(
resGR0 <- MRIaggr:::GRalgo(contrast=carto$T2_FLAIR_t2, W=W,
seed=indexN,sigma_max=500,range=c(300,+Inf),
breaks=seq(min(carto$T2_FLAIR_t2)-20,20+max(carto$T2_FLAIR_t2),by=20),
step=20,operator="sd",
iter_max=50,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
)
multiplot(coords,carto$T2_FLAIR_t2,
index1=coords[resGR0$GR,])
index_tree <- 7
multiplot(coords[resGR0$GR,],
as.numeric(unlist(lapply(strsplit(resGR0$history_front,split=".",fixed=TRUE),"[",index_tree)))
)
tree <- strsplit(resGR0$history_front,split=".",fixed=TRUE)
stepTree <- sapply(1:resGR0$iter,function(x){as.numeric(unlist(lapply(tree,"[",x)))})
head(stepTree)
apply(stepTree,2,table)
table(resGR0$history_step)
#### 2- init ####
#### >a initGR - private ####
# ?initGR
# MRIaggr:::initGR
# function(contrast,W,seed,range,range.seed,
# coords,sd.iter,breaks,scale,trace,method="initGR")
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
res.init <- MRIaggr:::initGR(contrast=carto$TTP_t0,W=W,seed=seed,range=c(-Inf,Inf),range.seed=c(-Inf,Inf),
breaks=100,scale=FALSE,trace=TRUE,method="initGR")
#### example
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#%%%%%
#%%%%% Fichier test 2.4 : Test des fonctions GR
#%%%%%
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
require(MRIaggr)
options(error=function() traceback(2))
options(max.print=10000)
#### 1- calc ####
#### >a calcCriteriaGR - private ####
# ?calcCriteriaGR
# MRIaggr:::calcCriteriaGR
# function(contrast,groups,W=NULL,sigma=NULL,breaks,
# criterion_d1=FALSE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE){
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
resGR_0.8 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=0.8,
iter_max=20)
resEvalGR_0.8 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_0.8$GR,W=W,sigma=0.8,breaks=resGR_0.8$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_0.8
resGR_1 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=20)
resEvalGR_1 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_1$GR,W=W,sigma=1,breaks=resGR_1$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_1
resGR_2 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=2,
iter_max=20)
resEvalGR_2 <- MRIaggr:::calcCriteriaGR(contrast=carto$TTP_t0,groups=1:nrow(carto) %in% resGR_2$GR,W=W,sigma=2,breaks=resGR_2$breaks,
criterion_d1=TRUE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE)
resEvalGR_2
#### example
#### >b calcGR ####
# ?calcGR
# calcGR
# function(contrast,W,seed,sigma_max,range=c(-Inf,+Inf),range.seed=c(-Inf,+Inf),coords=NULL,sd.iter=4,
# nbreaks=100,breaks=NULL,scale=FALSE,iter_max=100,sd.robust=FALSE,trace=TRUE,
# history_sigma=FALSE,history_step=FALSE,history_front=FALSE)
#### test
resGR0 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=20,
range=c(7,10))
# display
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR0$GR,],pch=20,cex=1),
index2=list(coords=coords[seed,],pch=20,cex=1)
)
#### test sphere
# n <- 100
# for(iter in c(10,20,30,40,50)){
# radius <- iter
# coords <- cbind(expand.grid(1:n,1:n),1)
# contrast <- as.numeric(sqrt((coords[,1]-n/2)^2+(coords[2]-n/2)^2)<radius)
# # W <- calcW(coords,range=sqrt(2),upper=NULL)
#
# # multiplot(coords,
# # contrast)
#
# resGR0 <- calcGR(contrast=contrast, W=W,
# seed=which( (coords[,1]==50)*(coords[,2]==50) == 1 ),
# sigma_max=10,range=c(0.5,+Inf),
# iter_max=50,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
#
# # multiplot(coords,contrast,
# # index1=coords[resGR0$GR,],main=iter)
#
# mean_step <- mean(resGR0$history_step)
# sd_step <- sd(resGR0$history_step)
#
# multiplot(coords[resGR0$GR,],(resGR0$history_step)/sd_step,
# main=iter)
# }
# hist(resGR0$history_step)
#
# IC_step <- mean_step + c(-1,1)*sd_step*qnorm(0.975)
#### example
## load an MRIaggr object
data(MRIaggr.Pat1_red, package="MRIaggr")
## display raw parameter
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,
breaks=seq(0,10,0.1),as.logical=TRUE,
index1=list(coords="MASK_DWI_t0",outline=TRUE))
## extract raw parameter, coordinates and compute the neighborhood matrix
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
## the seed is taken to be the point with the largest TTP in the lesion mask
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
## Display step by step the GR algorithm with sigma = 1
for(iter in c(0,1,2,5,10,15)){
resGR1 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=1,
iter_max=iter,trace=FALSE)
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR1$GR,],pch=20,cex=1),
index2=list(coords=coords[seed,],pch=20,cex=1)
)
}
## GR with sigma = 0.8
resGR2 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,sigma_max=0.8,iter_max=50,
history_step=TRUE,history_front=TRUE)
## display
# display the GR over the raw contrast
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR2$GR,],pch=20,cex=1)
)
# display the step of inclusion in GR group for each observation
multiplot(coords[resGR2$GR,],
resGR2$history_step,breaks=0:10,
index1=list(coords=coords[seed,]),
palette=rainbow(10)
)
# display the front propagation
multiplot(coords[resGR2$GR,],
resGR2$Mfront[,10],
index1=list(coords=coords[seed,])
)
## GR with sigma = 2
resGR3 <- calcGR(contrast=carto$TTP_t0, W=W,
seed=seed,
sigma_max=2,
iter_max=20)
## display
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR3$GR,],pch=20,cex=1)
)
#### >c calcSigmaGR ####
# ?calcSigmaGR
# calcSigmaGR
# function(contrast,W,seed,sigma,range=c(-Inf,+Inf),range.seed=c(-Inf,+Inf),
# breaks=100,scale=FALSE,iter_max=100,sd.robust=FALSE,
# criterion_d1=FALSE,criterion_entropy=TRUE,criterion_Kalinsky=TRUE,criterion_Laboure=TRUE,
# trace=TRUE,mar=rep(2,4),mgp=c(2,0.75,0),main="",
# window=FALSE,filename="calcSigmaGR",width=1000,height=700,path=NULL,unit="px",res=NA)
#### test
#### example
## load an MRIaggr object
data(MRIaggr.Pat1_red, package="MRIaggr")
## select data
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
## find optimal sigma
resGR_sigma <- calcSigmaGR(contrast=carto$TTP_t0, W=W,seed=seed,
sigma=seq(0.8,2,0.1),iter_max=50)
#### >d GRalgo - private ####
# ?GRalgo
# MRIaggr:::GRalgo
# function (contrast, W, seed, sigma_max, range, breaks,
# step, operator, iter_max, history)
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,
breaks=seq(0,10,0.1),as.logical=TRUE,
index1=list(coords="MASK_DWI_t0",outline=TRUE))
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0","T2_FLAIR_t2"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
# display
resGR0 <- MRIaggr:::GRalgo(contrast=carto$TTP_t0, W=W,
seed=indexN,sigma_max=1,range=c(7,+Inf),
breaks=seq(-5,40,1),step=1,operator="sd",
iter_max=10,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
multiplot(MRIaggr.Pat1_red,param="TTP_t0",num=3,hemisphere="lesion",legend=FALSE,
breaks=seq(0,10,0.1),as.logical=TRUE,ylim=c(25,45), xlim=c(75,95),cex=2,
index1=list(coords=coords[resGR0$GR,],pch=20,cex=1),
index2=list(coords=coords[indexN,],pch=20,cex=1)
)
multiplot(coords[resGR0$GR,],
as.numeric(unlist(lapply(strsplit(resGR0$history_front,split=".",fixed=TRUE),"[",1)))
)
#### T2
carto <- selectContrast(MRIaggr.Pat1_red,param=c("TTP_t0","MASK_DWI_t0","T2_FLAIR_t2"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
multiplot(MRIaggr.Pat1_red,param="T2_FLAIR_t2",hemisphere="lesion")
system.time(
resGR0 <- MRIaggr:::GRalgo(contrast=carto$T2_FLAIR_t2, W=W,
seed=indexN,sigma_max=500,range=c(300,+Inf),
breaks=seq(min(carto$T2_FLAIR_t2)-20,20+max(carto$T2_FLAIR_t2),by=20),
step=20,operator="sd",
iter_max=50,history_sigma=FALSE,history_step=TRUE,history_front=TRUE)
)
multiplot(coords,carto$T2_FLAIR_t2,
index1=coords[resGR0$GR,])
index_tree <- 7
multiplot(coords[resGR0$GR,],
as.numeric(unlist(lapply(strsplit(resGR0$history_front,split=".",fixed=TRUE),"[",index_tree)))
)
tree <- strsplit(resGR0$history_front,split=".",fixed=TRUE)
stepTree <- sapply(1:resGR0$iter,function(x){as.numeric(unlist(lapply(tree,"[",x)))})
head(stepTree)
apply(stepTree,2,table)
table(resGR0$history_step)
#### 2- init ####
#### >a initGR - private ####
# ?initGR
# MRIaggr:::initGR
# function(contrast,W,seed,range,range.seed,
# coords,sd.iter,breaks,scale,trace,method="initGR")
#### test
data(MRIaggr.Pat1_red, package="MRIaggr")
carto <- selectContrast(MRIaggr.Pat1_red,num=3,param=c("TTP_t0","MASK_DWI_t0"),hemisphere="lesion")
coords <- selectCoords(MRIaggr.Pat1_red,num=3,hemisphere="lesion")
W <- calcW(coords,range=sqrt(2))
indexN <- which(carto$MASK_DWI_t0==1)
seed <- indexN[which.max(carto[indexN,"TTP_t0"])]
res.init <- MRIaggr:::initGR(contrast=carto$TTP_t0,W=W,seed=seed,range=c(-Inf,Inf),range.seed=c(-Inf,Inf),
breaks=100,scale=FALSE,trace=TRUE,method="initGR")
#### example
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