#n/b these packages MUST be installed prior to analysis!
#install.packages("oro.dicom")
#install.packages("oro.nifti")
#source("http://bioconductor.org/biocLite.R")
#biocLite("EBImage")
#install.packages("stringi")
library("oro.dicom")
library("oro.nifti")
library("stringi")
require(EBImage)
library(parallel)
scansPath <- "scans/"
picdir <- "results/scans/face/"
resultLabelsPath<-"results/labels/face/"
resultLabelsFile = paste0(resultLabelsPath, "data_face.csv")
labelsPath <- "labels.csv"
patientLabelsPath <- "results/labels/face/"
dir.create(picdir, recursive = TRUE)
dir.create(resultLabelsPath, recursive = TRUE)
dir.create(patientLabelsPath, recursive = TRUE)
patientDir <- sort(list.dirs(path = scansPath, full.names = TRUE, recursive = FALSE))
pat.names <- sort(list.dirs(path = scansPath, full.names = F, recursive = FALSE))
labels<-read.csv(file = labelsPath)
data.sample<-matrix(data = NA,nrow = length(patientDir),ncol = 113)
# vectorized call of picToX function
call.picToX<-function(vect)
{
do.call(picToX,vect)
}
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
#patient i proceeding function
proceed.head<-function(pat.id)
{
print(pat.id)
tryCatch({
dicom <- readDICOM(patientDir[pat.id])
dicomdf <- dicomTable(dicom$hdr)
ps<-as.numeric(stri_split_fixed(dicomdf$`0028-0030-PixelSpacing`[1],pattern = " ")[[1]][1])
sl<-sort(as.numeric((dicomdf$`0020-1041-SliceLocation`)))
sln<-as.numeric((dicomdf$`0020-1041-SliceLocation`))
ss<-sl[2]-sl[1]
if(is.na(ss))
{
poss<-stri_split_fixed(dicomdf$`0020-0032-ImagePositionPatient`,pattern = " ")
sln<-array(0,dim = length(poss))
for(i in 1:length(poss))
{
sln[i]<-as.numeric(poss[[i]][3])
}
sl<-sort(sln)
ss<-sl[2]-sl[1]
}
dicom2nifti <- dicom2nifti(dicom,rescale = T, reslice = T)
hval = hist(dicom2nifti,breaks = 15)$density[1:7]
slope = scl_slope(dicom2nifti)
intercept = scl_inter(dicom2nifti)
vect<-array(data = list(),dim = dim(dicom2nifti)[2])
for(i in 1:dim(dicom2nifti)[2])
{
vect[[i]]$img = dicom2nifti[,i,]
vect[[i]]$slope = as.numeric(slope)
vect[[i]]$intercept = as.numeric(intercept)
vect[[i]]$bone.th = 1000
vect[[i]]$kern =makeBrush(5, shape='Gaussian',sigma = 0.3)
vect[[i]]$size.x = dim(dicom2nifti)[1]
vect[[i]]$size.y = dim(dicom2nifti)[3]
vect[[i]]$cbrush = 5
vect[[i]]$bin.th = -9999
sg<-0
vect[[i]]$RU=1*(sg<=0.6666)
vect[[i]]$LU=1*(sg<=0.5000)
vect[[i]]$voxelspacing = (ps+ss)/2
vect[[i]]$z=2
#print( vect[[i]]$RU)
#print( vect[[i]]$LU)
}
# vectorized (on cpu only) filtration of all of the 3d images for a given patient (map)
switch(Sys.info()[['sysname']],
Windows= {res<-lapply(X = vect,FUN = call.picToX)},
Linux = {res<-lapply(X = vect,FUN = call.picToX)},
Darwin = {res<-lapply(X = vect,FUN = call.picToX)})
stat<-labels[which(labels[,1]==pat.names[pat.id]),2]
if(length(stat)==0)
stat = "999"
#analyze the results (reduce) and choose the slice with the largest area of the tumor
# or average the results or choose other objective
ocsv<-NULL
rmax<-c(-Inf,-Inf,-Inf)
imax<-1
for(i in 1:length(res))
{
if(length(res[[i]])==0)
next
if(length(res[[i]]$X)==0)
next
ocsv<-cbind(ocsv,c(i,hval,g(res[[i]]$X[1]),g(res[[i]]$X[2]),g(res[[i]]$X[3]),sum(sl>sln[i])/length(sl),res[[i]]$X,ps,ps^2,1/ps,1/ps^2,ss,1/ss,ss*ps,1/ss/ps,stat))
rcur<-res[[i]]$X[c(1,2,3,10)]
if(rcur[1]>rmax[1] & rcur[2]>rmax[2] & rcur[3]>rmax[3] & rcur[4] < 2500)
{
rmax = rcur[-4]
imax = i
}
}
if(length(ocsv)!=0)
write.csv(file = paste0(patientLabelsPath, "data_face_",pat.id,"_",imax,".csv"),x = ocsv)
if(length(res[[imax]])==0|length(res[[imax]]$lungs)==0|length(res[[imax]]$nodules)==0|length(res[[imax]]$lungs)==0)
{
warning(paste0("Patient ",pat.id,"not proceeded correctly"))
rm(vect)
rm(dicom)
rm(dicomdf)
return(NULL)
}
if(doPNG <- !dev.interactive())
png(paste0(picdir,"nodules_f_",pat.id,"_",stat,".png"), width=512, height=512)
image(res[[imax]]$nodules, useRaster = F,col = grey(0:64 / 64)) # should not suffer from anti-aliasing
if(doPNG)
dev.off()
if(doPNG <- !dev.interactive())
png(paste0(picdir,"lungs_f_",pat.id,"_",stat,".png"), width=512, height=512)
image(res[[imax]]$lungs, useRaster = F,col = grey(0:64 / 64)) # should not suffer from anti-aliasing
if(doPNG)
dev.off()
if(doPNG <- !dev.interactive())
png(paste0(picdir,"img_f_",pat.id,"_",stat,".png"), width=512, height=512)
image(vect[[imax]]$img, useRaster = F,col = grey(0:64 / 64)) # should not suffer from anti-aliasing
if(doPNG)
dev.off()
rm(vect)
rm(dicom)
rm(dicomdf)
return(list(X = c(g(res[[imax]]$X[1]),g(res[[imax]]$X[2]),g(res[[imax]]$X[3]),hval,sum(sl>sln[imax])/length(sl),res[[imax]]$X,ps,ps^2,1/ps,1/ps^2,ss,1/ss,ss*ps,1/ss/ps,stat), imax = imax,lungs = res[[imax]]$lungs,nodules = res[[imax]]$nodules))},
error=function(e)
{
print(e)
return(NULL)
})
}
switch(Sys.info()[['sysname']],
Windows= {data = lapply(FUN = proceed.head,X = 1:length(patientDir))},
Linux = {data = mclapply(FUN = proceed.head,X = 1:length(patientDir),mc.preschedule = F,mc.cores = 4,mc.cleanup = T)},
Darwin = {data = lapply(FUN = proceed.head,X = 1:length(patientDir))})
for(i in 1:length(patientDir))
{
if(length(data[[i]])==0|length(data[[i]]$X)==0)
{
warning(paste0("Patient ",i,"not proceeded correctly"))
next
}
data.sample[i,]<-data[[i]]$X
#print(data[[i]]$imax)
}
write.csv(file = resultLabelsFile,x = data.sample)
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