#' Calculate first-order, GLCM, and/or GLRLM radiomic features on the whole 3D lung
#'
#' This is a wrapper for \code{RIA} R package. It calculates first-order, GLCM, and/or GLRLM on the whole 3D lung, left and right lungs separately
#'
#' @param img CT scan in ANTs image file format
#' @param mask Mask of CT scan in ANTs image file format
#' @param sides Choose to calculate radiomic features on the right and/or left lungs. Note: Right lung = 1, left lung = 2, non-lung = 0
#' @param features Type of radiomic features to calculate. Options: first-order, GLCM, and/or GLRLM
#' @param bins_in Number of bins to discretize image
#' @param equal_prob logical, indicating to cut data into bins with equal relative frequencies.
#' If FALSE, then equal interval bins will be used.
#' @param distance integer, distance between the voxels being compared.
#' @param statistic string, defining the statistic to be calculated on the array of GLCM statistics.
#' By default, statistic is set to \emph{"mean"}, however any function may be provided. The proper
#' syntax is: function(X, attributes). The supplied string must contain a "X", which will be replaced
#' with the array of the GLCM statistics value. Further attributes of the function may also be given.
#' For example, if you wish to calculate the median of all GLCMs calculated in different directions,
#' then it must be supplied as: \emph{median(X, na.rm = TRUE)}.
#' @param verbose_in logical, indicating whether to print detailed information.
#' Most prints can also be suppressed using the \code{\link{suppressMessages}} function.
#'
#' @return list containing the statistical information
#' @export
# #' @importFrom RIA first_order discretize glcm_all glcm_stat glcm_stat_all glrlm_all glrlm_stat glrlm_stat_all
RIA_lung <- function(img,
mask,
sides = c("right", "left"),
features = c('fo', 'glcm', 'glrlm'),
bins_in = 8,
equal_prob = FALSE,
distance = 1,
statistic = "mean(X, na.rm = TRUE)",
verbose_in = TRUE){
if (!requireNamespace("RIA", quietly = TRUE)) {
stop("RIA package required for RIA_lung")
}
# Loop through mask values
featuresMask <- lapply(sides, function(side){
if(side == "right"){mv = 1}
if(side == "left"){mv = 2}
# Put image in array format and remove non-mask values
data <- as.array(img)
mask2 <- as.array(mask)
data[mask2 != mv] <- NA
# Crop image to speed up computation
test <- apply(data, 1, function(x){sum(x, na.rm=T)})
data <- data[which(test != 0),,]
test <- apply(data, 2, function(x){sum(x, na.rm=T)})
data <- data[,which(test != 0),]
test <- apply(data, 3, function(x){sum(x, na.rm=T)})
data <- data[,,which(test != 0)]
###create RIA_image structure
RIA_image <- list(data = NULL, header = list(), log = list())
if(length(dim(data)) == 3 | length(dim(data)) == 2) {class(RIA_image) <- append(class(RIA_image), "RIA_image")
} else {stop(paste0("ANTsImage LOADED IS ", length(dim(data)), " DIMENSIONAL. ONLY 2D AND 3D DATA ARE SUPPORTED!"))}
RIA_image$data$orig <- data
RIA_image$data$modif <- NULL
class(RIA_image$header) <- append(class(RIA_image$header), "RIA_header")
class(RIA_image$data) <- append(class(RIA_image$data), "RIA_data")
class(RIA_image$log) <- append(class(RIA_image$log), "RIA_log")
RIA_image$log$events <- "Created"
RIA_image$log$orig_dim <- dim(data)
# Calculate first order radiomic features
if('fo' %in% features){
RIA_image <- RIA::first_order(RIA_image, use_type = "single", use_orig = TRUE, verbose_in = verbose_in)
}
# Discretize image
if('glcm' %in% features | 'glrlm' %in% features){
RIA_image <- RIA::discretize(RIA_image, bins_in=bins_in, equal_prob = equal_prob, verbose_in = verbose_in)
# Calculate GLCM radiomic features
if('glcm' %in% features){
for (i in 1: length(distance)) {
RIA_image <- RIA::glcm_all(RIA_image, use_type = "discretized", distance = distance[i], verbose_in = verbose_in)
}
RIA_image <- RIA::glcm_stat(RIA_image, use_type = "glcm", verbose_in = verbose_in)
RIA_image <- RIA::glcm_stat_all(RIA_image, statistic = statistic, verbose_in = verbose_in)
}
# Calculate GLRLM radiomic features
if('glrlm' %in% features){
RIA_image <- RIA::glrlm_all(RIA_image, use_type = "discretized", verbose_in = verbose_in)
RIA_image <- RIA::glrlm_stat(RIA_image, use_type = "glrlm", verbose_in = verbose_in)
RIA_image <- RIA::glrlm_stat_all(RIA_image, statistic = statistic, verbose_in = verbose_in)
}else{RIA_image$stat_glrlm_mean <- NULL}
}
features <- list(first_order = RIA_image$stat_fo$orig,
glcm = RIA_image$stat_glcm_mean,
glrlm = RIA_image$stat_glrlm_mean)
return(features)
})
names(featuresMask) <- sides
return(featuresMask)
}
