integrate_result.R

# Integrate X images from X = 1 to 91
# For BFs, Means, and Medians

require("oro.nifti")

# To get a header, read one original file in the list (First item)
# Read filelist
data<-read.csv("list.csv")
filecount <-nrow(data)

# Get the first file
filename <- toString(data[1,1])
# Read the current nifti
Img <- readNIfTI(filename)
# Extract current image data
ImgData = oro.nifti::img_data(Img)

# Start with BFs.
for (i in 1:91){
	filename <- sprintf("./Outputs/BF%d.Rdata",i)
	# Read the current RData.
	load (file =filename)
	# Attach current BFs
	for (j in 1:109){
		for (k in 1:91){
			ImgData[i,j,k] = BFs[j,k]
		}
	}
}

# Write BFs.nii
# Set Image data
oro.nifti::img_data(Img)<-ImgData
writeNIfTI(Img,"BFs",gzipped = FALSE)

# Then, Means
for (i in 1:91){
	filename <- sprintf("./Outputs/Mean%d.Rdata",i)
	# Read the current RData.
	load (file =filename)
	# Attach current Mean
	for (j in 1:109){
		for (k in 1:91){
			ImgData[i,j,k] = Means[j,k]
		}
	}
}

# Write Means.nii
# Set Image data
oro.nifti::img_data(Img)<-ImgData
writeNIfTI(Img,"Means",gzipped = FALSE)

# Then, Medians
for (i in 1:91){
	filename <- sprintf("./Outputs/Median%d.Rdata",i)
	# Read the current RData.
	load (file =filename)
	# Attach current Median
	for (j in 1:109){
		for (k in 1:91){
			ImgData[i,j,k] = Medians[j,k]
		}
	}
}

# Write Medians.nii
# Set Image data
oro.nifti::img_data(Img)<-ImgData
writeNIfTI(Img,"Medians",gzipped = FALSE)