R/glcm_stat.R
In neuroconductor/RIA: Radiomics Image Analysis Toolbox for Medial Images
Defines functions
glcm_stat
Documented in
glcm_stat
#' @title Calculates GLCM-based statistics
#' @export
#'
#' @description Calculates GLCM-based statistics for given GLCM matrix.
#'
#' @param RIA_data_in \emph{RIA_image}.
#'
#' @param use_type string, can be \emph{"single"} which runs the function on a single image,
#' which is determined using \emph{"use_orig"} or \emph{"use_slot"}. \emph{"glcm"}
#' takes all datasets in the \emph{RIA_image$glcm} slot and runs the analysis on them.
#'
#' @param use_orig logical, indicating to use image present in \emph{RIA_data$orig}.
#' If FALSE, the modified image will be used stored in \emph{RIA_data$modif}. However, GLCM matrices
#' are usually not present in either slots, therefore giving the slot name using \emph{use_slot} is advised.
#'
#' @param use_slot string, name of slot where data wished to be used is. Use if the desired image
#' is not in the \emph{data$orig} or \emph{data$modif} slot of the \emph{RIA_image}. For example,
#' ig the desired dataset is in \emph{RIA_image$glcm$ep_4_111}, then \emph{use_slot} should be
#' \emph{glcm$ep_4_111}. The results are automatically saved. If the results are not saved to
#' the desired slot, then please use \emph{save_name} parameter. If the string contains "-" characters
#' use "`" before the last slot name, for example: \emph{glcm$`ep_4_-1-1-1`}
#'
#' @param save_name string, indicating the name of subslot of \emph{$glcm} to save results to.
#' If left empty, then it will be automatically determined.
#'
#' @param verbose_in logical, indicating whether to print detailed information.
#' Most prints can also be suppressed using the \code{\link{suppressMessages}} function.
#'
#' @return \emph{RIA_image} containing the statistical information.
#'
#' @examples \dontrun{
#' #Discretize loaded image and then calculate GLCM statistics
#' RIA_image <- discretize(RIA_image, bins_in = 8, equal_prob = TRUE)
#' RIA_image <- glcm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_8",
#' off_right = 0, off_down = 1, off_z = 0)
#' RIA_image <- glcm_stat(RIA_image, use_orig = FALSE, use_slot = "glcm$ep_8_010")
#'
#' #Batch calculation of GLCM-based statistics on all calculated GLCMs
#' RIA_image <- glcm_stat(RIA_image, use_type = "discretized")
#' }
#'
#' @references Márton KOLOSSVÁRY et al.
#' Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic
#' Metrics to Identify Coronary Plaques With Napkin-Ring Sign
#' Circulation: Cardiovascular Imaging (2017).
#' DOI: 10.1161/circimaging.117.006843
#' \url{https://www.ncbi.nlm.nih.gov/pubmed/29233836}
#'
#' Márton KOLOSSVÁRY et al.
#' Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques.
#' Journal of Thoracic Imaging (2018).
#' DOI: 10.1097/RTI.0000000000000268
#' \url{https://www.ncbi.nlm.nih.gov/pubmed/28346329}
#' @encoding UTF-8
glcm_stat <- function(RIA_data_in, use_type = "single", use_orig = FALSE, use_slot = "glcm$es_8_111", save_name = NULL, verbose_in = TRUE)
{
data_in_orig <- check_data_in(RIA_data_in, use_type = use_type, use_slot = use_slot, verbose_in = verbose_in)
if(any(class(data_in_orig) != "list")) data_in_orig <- list(data_in_orig)
list_names <- names(data_in_orig)
if(!is.null(save_name) & (length(data_in_orig) != length(save_name))) {stop(paste0("PLEASE PROVIDE THE SAME NUMBER OF NAMES AS THERE ARE IMAGES!\n",
"NUMBER OF NAMES: ", length(save_name), "\n",
"NUMBER OF IMAGES: ", length(data_in), "\n"))
}
for (i in 1: length(data_in_orig))
{
data_in <- data_in_orig[[i]]
data_NA <- as.vector(data_in)
data_NA <- data_NA[!is.na(data_NA)]
if(length(data_NA) == 0) {message("WARNING: SUPPLIED RIA_image DOES NOT CONTAIN ANY DATA!!!")}
Contrast <- contrast(data_in)
Contrast_s <- contrast(data_in, type_in = "squared")
Contrast_e <- contrast(data_in, type_in = "entropy")
Homogeneity2 <- homogeneity2(data_in)
Homogeneity2_s <- homogeneity2(data_in, type_in = "squared")
Homogeneity2_e <- homogeneity2(data_in, type_in = "entropy")
Homogeneity2_nd <- homogeneity2(data_in, diag = FALSE)
Homogeneity2_s_nd <- homogeneity2(data_in, type_in = "squared", diag = FALSE)
Homogeneity2_e_nd <- homogeneity2(data_in, type_in = "entropy", diag = FALSE)
Dissimilarity <- dissimilarity(data_in)
Dissimilarity_s <- dissimilarity(data_in, type_in = "squared")
Dissimilarity_e <- dissimilarity(data_in, type_in = "entropy")
Homogeneity1 <- homogeneity1(data_in)
Homogeneity1_s <- homogeneity1(data_in, type_in = "squared")
Homogeneity1_e <- homogeneity1(data_in, type_in = "entropy")
Homogeneity1_nd <- homogeneity1(data_in, diag = FALSE)
Homogeneity1_s_nd <- homogeneity1(data_in, type_in = "squared", diag = FALSE)
Homogeneity1_e_nd <- homogeneity1(data_in, type_in = "entropy", diag = FALSE)
DMN <- dmn(data_in)
DMN_s <- dmn(data_in, type_in = "squared")
DMN_e <- dmn(data_in, type_in = "entropy")
IDMN <- idmn(data_in)
IDMN_s <- idmn(data_in, type_in = "squared")
IDMN_e <- idmn(data_in, type_in = "entropy")
IDMN_nd <- idmn(data_in, diag = FALSE)
IDMN_s_nd <- idmn(data_in, type_in = "squared", diag = FALSE)
IDMN_e_nd <- idmn(data_in, type_in = "entropy", diag = FALSE)
DN <- dn(data_in)
DN_s <- dn(data_in, type_in = "squared")
DN_e <- dn(data_in, type_in = "entropy")
IDN <- idn(data_in)
IDN_s <- idn(data_in, type_in = "squared")
IDN_e <- idn(data_in, type_in = "entropy")
IDN_nd <- idn(data_in, diag = FALSE)
IDN_s_nd <- idn(data_in, type_in = "squared", diag = FALSE)
IDN_e_nd <- idn(data_in, type_in = "entropy", diag = FALSE)
Autocorrelation <- autocorrelation(data_in)
Autocorrelation_s <- autocorrelation(data_in, type_in = "squared")
Autocorrelation_e <- autocorrelation(data_in, type_in = "entropy")
Autocorrelation_nd <- autocorrelation(data_in, diag = FALSE)
Autocorrelation_s_nd <- autocorrelation(data_in, type_in = "squared", diag = FALSE)
Autocorrelation_e_nd <- autocorrelation(data_in, type_in = "entropy", diag = FALSE)
Inv_autocorrelation <- inv_autocorrelation(data_in)
Inv_autocorrelation_s <- inv_autocorrelation(data_in, type_in = "squared")
Inv_autocorrelation_e <- inv_autocorrelation(data_in, type_in = "entropy")
Inv_autocorrelation_nd <- inv_autocorrelation(data_in, diag = FALSE)
Inv_autocorrelation_s_nd <- inv_autocorrelation(data_in, type_in = "squared", diag = FALSE)
Inv_autocorrelation_e_nd <- inv_autocorrelation(data_in, type_in = "entropy", diag = FALSE)
Gauss <- gauss(data_in)
Gauss_s <- gauss(data_in, type_in = "squared")
Gauss_e <- gauss(data_in, type_in = "entropy")
Gauss_nd <- gauss(data_in, diag = FALSE)
Gauss_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE)
Gauss_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE)
Gauss_lp <- gauss(data_in, loc = 1)
Gauss_lp_s <- gauss(data_in, type_in = "squared", loc = 1)
Gauss_lp_e <- gauss(data_in, type_in = "entropy", loc = 1)
Gauss_lp_nd <- gauss(data_in, diag = FALSE, loc = 1)
Gauss_lp_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 1)
Gauss_lp_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 1)
Gauss_lf <- gauss(data_in, loc = 2)
Gauss_lf_s <- gauss(data_in, type_in = "squared", loc = 2)
Gauss_lf_e <- gauss(data_in, type_in = "entropy", loc = 2)
Gauss_lf_nd <- gauss(data_in, diag = FALSE, loc = 2)
Gauss_lf_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 2)
Gauss_lf_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 2)
Gauss_rf <- gauss(data_in, loc = 4)
Gauss_rf_s <- gauss(data_in, type_in = "squared", loc = 4)
Gauss_rf_e <- gauss(data_in, type_in = "entropy", loc = 4)
Gauss_rf_nd <- gauss(data_in, diag = FALSE, loc = 4)
Gauss_rf_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 4)
Gauss_rf_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 4)
Gauss_rp <- gauss(data_in, loc = 5)
Gauss_rp_s <- gauss(data_in, type_in = "squared", loc = 5)
Gauss_rp_e <- gauss(data_in, type_in = "entropy", loc = 5)
Gauss_rp_nd <- gauss(data_in, diag = FALSE, loc = 5)
Gauss_rp_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 5)
Gauss_rp_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 5)
Inv_Gauss <- gauss(data_in, inverse = TRUE)
Inv_Gauss_s <- gauss(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_e <- gauss(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_nd <- gauss(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Inv_Gauss_lp <- gauss(data_in, inverse = TRUE, loc = 1)
Inv_Gauss_lp_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 1)
Inv_Gauss_lp_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 1)
Inv_Gauss_lp_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 1)
Inv_Gauss_lp_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 1)
Inv_Gauss_lp_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 1)
Inv_Gauss_lf <- gauss(data_in, inverse = TRUE, loc = 2)
Inv_Gauss_lf_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 2)
Inv_Gauss_lf_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 2)
Inv_Gauss_lf_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 2)
Inv_Gauss_lf_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 2)
Inv_Gauss_lf_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 2)
Inv_Gauss_rf <- gauss(data_in, inverse = TRUE, loc = 4)
Inv_Gauss_rf_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 4)
Inv_Gauss_rf_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 4)
Inv_Gauss_rf_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 4)
Inv_Gauss_rf_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 4)
Inv_Gauss_rf_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 4)
Inv_Gauss_rp <- gauss(data_in, inverse = TRUE, loc = 5)
Inv_Gauss_rp_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 5)
Inv_Gauss_rp_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 5)
Inv_Gauss_rp_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 5)
Inv_Gauss_rp_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 5)
Inv_Gauss_rp_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 5)
Gauss_2f <- gauss2f(data_in)
Gauss_2f_s <- gauss2f(data_in, type_in = "squared")
Gauss_2f_e <- gauss2f(data_in, type_in = "entropy")
Gauss_2f_nd <- gauss2f(data_in, diag = FALSE)
Gauss_2f_s_nd <- gauss2f(data_in, type_in = "squared", diag = FALSE)
Gauss_2f_e_nd <- gauss2f(data_in, type_in = "entropy", diag = FALSE)
Inv_Gauss_2f <- gauss2f(data_in, inverse = TRUE)
Inv_Gauss_2f_s <- gauss2f(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_2f_e <- gauss2f(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_2f_nd <- gauss2f(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_2f_s_nd <- gauss2f(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_2f_e_nd <- gauss2f(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Gauss_2p <- gauss2p(data_in)
Gauss_2p_s <- gauss2p(data_in, type_in = "squared")
Gauss_2p_e <- gauss2p(data_in, type_in = "entropy")
Gauss_2p_nd <- gauss2p(data_in, diag = FALSE)
Gauss_2p_s_nd <- gauss2p(data_in, type_in = "squared", diag = FALSE)
Gauss_2p_e_nd <- gauss2p(data_in, type_in = "entropy", diag = FALSE)
Inv_Gauss_2p <- gauss2p(data_in, inverse = TRUE)
Inv_Gauss_2p_s <- gauss2p(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_2p_e <- gauss2p(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_2p_nd <- gauss2p(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_2p_s_nd <- gauss2p(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_2p_e_nd <- gauss2p(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_p <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_p_s <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_p_e <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_p_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_p_s_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_p_e_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_p <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_p_s <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_p_e <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_p_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_p_s_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_p_e_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_s <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_s_s <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_s_e <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_s_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_s_s_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_s_e_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_s <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_s_s <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_s_e <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_s_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_s_s_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_s_e_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_t <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_t_s <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_t_e <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_t_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_t_s_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_t_e_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_t <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_t_s <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_t_e <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_t_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_t_s_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_t_e_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_d <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_d_s <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_d_e <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_d_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_d_s_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_d_e_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_d <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_d_s <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_d_e <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_d_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_d_s_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_d_e_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "entropy", diag = FALSE)
Average <- avg(data_in, type_in = "single")
Average_s <- avg(data_in, type_in = "squared")
Average_e <- avg(data_in, type_in = "entropy")
Variances <- variance(data_in, type_in = "single")
Variances_s <- variance(data_in, type_in = "squared")
Variances_e <- variance(data_in, type_in = "entropy")
Correlation <- correlation(data_in, type_in = "single")
Correlation_s <- correlation(data_in, type_in = "squared")
Correlation_e <- correlation(data_in, type_in = "entropy")
Sum_average <- sum_f(data_in, type_in = "single")
Sum_energy <- sum_f(data_in, type_in = "squared")
Sum_entropy <- sum_f(data_in, type_in = "entropy")
Sum_variance <- sum_f(data_in, type_in = "variance")
Dif_average <- dif_f(data_in, type_in = "single")
Dif_energy <- dif_f(data_in, type_in = "squared")
Dif_entropy <- dif_f(data_in, type_in = "entropy")
Dif_variance <- dif_f(data_in, type_in = "variance")
Inv_sum_average <- sum_f(data_in, inverse = TRUE, type_in = "single")
Inv_sum_energy <- sum_f(data_in, inverse = TRUE, type_in = "squared")
Inv_sum_variance <- sum_f(data_in, inverse = TRUE, type_in = "variance")
Inv_dif_average <- dif_f(data_in, inverse = TRUE, type_in = "single")
Inv_dif_energy <- dif_f(data_in, inverse = TRUE, type_in = "squared")
Inv_dif_variance <- dif_f(data_in, inverse = TRUE, type_in = "variance")
IMC1 <- imc1(data_in)
IMC2 <- imc2(data_in)
data <- as.vector(data_in)
data <- data[!is.na(data)]
Energy <- energy(data)
Entropy <- entropy(data, 2)
Mean <- base::mean(data)
Median <- stats::median(data)
Mode <- mode(data)[1]
Geo_mean <- geo_mean(data)
Geo_mean2 <- geo_mean2(data)
Geo_mean3 <- geo_mean3(data)
Har_mean <- har_mean(data)
Trim_mean_5 <- base::mean(data, trim = 0.025)
Trim_mean_10 <- base::mean(data, trim = 0.05)
Trim_mean_20 <- base::mean(data, trim = 0.1)
IQ_mean <- base::mean(data, trim = 0.25)
Tri_mean <- (as.numeric(stats::quantile(data, 0.25)) +2*as.numeric(stats::quantile(data, 0.50)) + as.numeric(stats::quantile(data, 0.25)))/4
Mn_AD_mn <- mn_AD_mn(data)
Mn_AD_md <- mn_AD_md(data)
Md_AD_mn <- md_AD_mn(data)
Md_AD_md <- md_AD_md(data)
MAD <- stats::mad(data)
Max_AD_mn <- max_AD_mn(data)
Max_AD_md <- max_AD_md(data)
RMS <- rms(data)
Min <- base::min(data)
Max <- base::max(data)
Quartiles <- stats::quantile(data, seq(0.25, 0.75, 0.50))
IQR <- stats::IQR(data)
Low_notch <- as.numeric(Quartiles[1])-1.5*IQR
High_notch <- as.numeric(Quartiles[1])+1.5*IQR
Range <- abs(abs(base::range(data)[2] - base::range(data)[1]))
Deciles <- stats::quantile(data, seq(0.1, 0.9, 0.1))
Variance <- ifelse(length(data)>1, stats::var(data), 0)
SD <- ifelse(length(data)>1, stats::sd(data), 0)
Skew <- ifelse(length(data)>1, skew(data), 0)
Kurtosis <- ifelse(length(data)>1, kurtosis(data), 0)
Uniformity <- uniformity(data)
metrics <- list(
Contrast <- Contrast,
Contrast_s <- Contrast_s,
Contrast_e <- Contrast_e,
Homogeneity2 <- Homogeneity2,
Homogeneity2_s <- Homogeneity2_s,
Homogeneity2_e <- Homogeneity2_e,
Homogeneity2_nd <- Homogeneity2_nd,
Homogeneity2_s_nd <- Homogeneity2_s_nd,
Homogeneity2_e_nd <- Homogeneity2_e_nd,
Dissimilarity <- Dissimilarity,
Dissimilarity_s <- Dissimilarity_s,
Dissimilarity_e <- Dissimilarity_e,
Homogeneity1 <- Homogeneity1,
Homogeneity1_s <- Homogeneity1_s,
Homogeneity1_e <- Homogeneity1_e,
Homogeneity1_nd <- Homogeneity1_nd,
Homogeneity1_s_nd <- Homogeneity1_s_nd,
Homogeneity1_e_nd <- Homogeneity1_e_nd,
DMN <- DMN,
DMN_s <- DMN_s,
DMN_e <- DMN_e,
IDMN <- IDMN,
IDMN_s <- IDMN_s,
IDMN_e <- IDMN_e,
IDMN_nd <- IDMN_nd,
IDMN_s_nd <- IDMN_s_nd,
IDMN_e_nd <- IDMN_e_nd,
DN <- DN,
DN_s <- DN_s,
DN_e <- DN_e,
IDN <- IDN,
IDN_s <- IDN_s,
IDN_e <- IDN_e,
IDN_nd <- IDN_nd,
IDN_s_nd <- IDN_s_nd,
IDN_e_nd <- IDN_e_nd,
Autocorrelation <- Autocorrelation,
Autocorrelation_s <- Autocorrelation_s,
Autocorrelation_e <- Autocorrelation_e,
Autocorrelation_nd <- Autocorrelation_nd,
Autocorrelation_s_nd <- Autocorrelation_s_nd,
Autocorrelation_e_nd <- Autocorrelation_e_nd,
Inv_autocorrelation <- Inv_autocorrelation,
Inv_autocorrelation_s <- Inv_autocorrelation_s,
Inv_autocorrelation_e <- Inv_autocorrelation_e,
Inv_autocorrelation_nd <- Inv_autocorrelation_nd,
Inv_autocorrelation_s_nd <- Inv_autocorrelation_s_nd,
Inv_autocorrelation_e_nd <- Inv_autocorrelation_e_nd,
Gauss <- Gauss,
Gauss_s <- Gauss_s,
Gauss_e <- Gauss_e,
Gauss_nd <- Gauss_nd,
Gauss_s_nd <- Gauss_s_nd,
Gauss_e_nd <- Gauss_e_nd,
Gauss_lp <- Gauss_lp,
Gauss_lp_s <- Gauss_lp_s,
Gauss_lp_e <- Gauss_lp_e,
Gauss_lp_nd <- Gauss_lp_nd,
Gauss_lp_s_nd <- Gauss_lp_s_nd,
Gauss_lp_e_nd <- Gauss_lp_e_nd,
Gauss_lf <- Gauss_lf,
Gauss_lf_s <- Gauss_lf_s,
Gauss_lf_e <- Gauss_lf_e,
Gauss_lf_nd <- Gauss_lf_nd,
Gauss_lf_s_nd <- Gauss_lf_s_nd,
Gauss_lf_e_nd <- Gauss_lf_e_nd,
Gauss_rf <- Gauss_rf,
Gauss_rf_s <- Gauss_rf_s,
Gauss_rf_e <- Gauss_rf_e,
Gauss_rf_nd <- Gauss_rf_nd,
Gauss_rf_s_nd <- Gauss_rf_s_nd,
Gauss_rf_e_nd <- Gauss_rf_e_nd,
Gauss_rp <- Gauss_rp,
Gauss_rp_s <- Gauss_rp_s,
Gauss_rp_e <- Gauss_rp_e,
Gauss_rp_nd <- Gauss_rp_nd,
Gauss_rp_s_nd <- Gauss_rp_s_nd,
Gauss_rp_e_nd <- Gauss_rp_e_nd,
Inv_Gauss <- Inv_Gauss,
Inv_Gauss_s <- Inv_Gauss_s,
Inv_Gauss_e <- Inv_Gauss_e,
Inv_Gauss_nd <- Inv_Gauss_nd,
Inv_Gauss_s_nd <- Inv_Gauss_s_nd,
Inv_Gauss_e_nd <- Inv_Gauss_e_nd,
Inv_Gauss_lp <- Inv_Gauss_lp,
Inv_Gauss_lp_s <- Inv_Gauss_lp_s,
Inv_Gauss_lp_e <- Inv_Gauss_lp_e,
Inv_Gauss_lp_nd <- Inv_Gauss_lp_nd,
Inv_Gauss_lp_s_nd <- Inv_Gauss_lp_s_nd,
Inv_Gauss_lp_e_nd <- Inv_Gauss_lp_e_nd,
Inv_Gauss_lf <- Inv_Gauss_lf,
Inv_Gauss_lf_s <- Inv_Gauss_lf_s,
Inv_Gauss_lf_e <- Inv_Gauss_lf_e,
Inv_Gauss_lf_nd <- Inv_Gauss_lf_nd,
Inv_Gauss_lf_s_nd <- Inv_Gauss_lf_s_nd,
Inv_Gauss_lf_e_nd <- Inv_Gauss_lf_e_nd,
Inv_Gauss_rf <- Inv_Gauss_rf,
Inv_Gauss_rf_s <- Inv_Gauss_rf_s,
Inv_Gauss_rf_e <- Inv_Gauss_rf_e,
Inv_Gauss_rf_nd <- Inv_Gauss_rf_nd,
Inv_Gauss_rf_s_nd <- Inv_Gauss_rf_s_nd,
Inv_Gauss_rf_e_nd <- Inv_Gauss_rf_e_nd,
Inv_Gauss_rp <- Inv_Gauss_rp,
Inv_Gauss_rp_s <- Inv_Gauss_rp_s,
Inv_Gauss_rp_e <- Inv_Gauss_rp_e,
Inv_Gauss_rp_nd <- Inv_Gauss_rp_nd,
Inv_Gauss_rp_s_nd <- Inv_Gauss_rp_s_nd,
Inv_Gauss_rp_e_nd <- Inv_Gauss_rp_e_nd,
Gauss_2f <- Gauss_2f,
Gauss_2f_s <- Gauss_2f_s,
Gauss_2f_e <- Gauss_2f_e,
Gauss_2f_nd <- Gauss_2f_nd,
Gauss_2f_s_nd <- Gauss_2f_s_nd,
Gauss_2f_e_nd <- Gauss_2f_e_nd,
Inv_Gauss_2f <- Inv_Gauss_2f,
Inv_Gauss_2f_s <- Inv_Gauss_2f_s,
Inv_Gauss_2f_e <- Inv_Gauss_2f_e,
Inv_Gauss_2f_nd <- Inv_Gauss_2f_nd,
Inv_Gauss_2f_s_nd <- Inv_Gauss_2f_s_nd,
Inv_Gauss_2f_e_nd <- Inv_Gauss_2f_e_nd,
Gauss_2p <- Gauss_2p,
Gauss_2p_s <- Gauss_2p_s,
Gauss_2p_e <- Gauss_2p_e,
Gauss_2p_nd <- Gauss_2p_nd,
Gauss_2p_s_nd <- Gauss_2p_s_nd,
Gauss_2p_e_nd <- Gauss_2p_e_nd,
Inv_Gauss_2p <- Inv_Gauss_2p,
Inv_Gauss_2p_s <- Inv_Gauss_2p_s,
Inv_Gauss_2p_e <- Inv_Gauss_2p_e,
Inv_Gauss_2p_nd <- Inv_Gauss_2p_nd,
Inv_Gauss_2p_s_nd <- Inv_Gauss_2p_s_nd,
Inv_Gauss_2p_e_nd <- Inv_Gauss_2p_e_nd,
Cluster_p <- Cluster_p,
Cluster_p_s <- Cluster_p_s,
Cluster_p_e <- Cluster_p_e,
Cluster_p_nd <- Cluster_p_nd,
Cluster_p_s_nd <- Cluster_p_s_nd,
Cluster_p_e_nd <- Cluster_p_e_nd,
Inv_Cluster_p <- Inv_Cluster_p,
Inv_Cluster_p_s <- Inv_Cluster_p_s,
Inv_Cluster_p_e <- Inv_Cluster_p_e,
Inv_Cluster_p_nd <- Inv_Cluster_p_nd,
Inv_Cluster_p_s_nd <- Inv_Cluster_p_s_nd,
Inv_Cluster_p_e_nd <- Inv_Cluster_p_e_nd,
Cluster_s <- Cluster_s,
Cluster_s_s <- Cluster_s_s,
Cluster_s_e <- Cluster_s_e,
Cluster_s_nd <- Cluster_s_nd,
Cluster_s_s_nd <- Cluster_s_s_nd,
Cluster_s_e_nd <- Cluster_s_e_nd,
Inv_Cluster_s <- Inv_Cluster_s,
Inv_Cluster_s_s <- Inv_Cluster_s_s,
Inv_Cluster_s_e <- Inv_Cluster_s_e,
Inv_Cluster_s_nd <- Inv_Cluster_s_nd,
Inv_Cluster_s_s_nd <- Inv_Cluster_s_s_nd,
Inv_Cluster_s_e_nd <- Inv_Cluster_s_e_nd,
Cluster_t <- Cluster_t,
Cluster_t_s <- Cluster_t_s,
Cluster_t_e <- Cluster_t_e,
Cluster_t_nd <- Cluster_t_nd,
Cluster_t_s_nd <- Cluster_t_s_nd,
Cluster_t_e_nd <- Cluster_t_e_nd,
Inv_Cluster_t <- Inv_Cluster_t,
Inv_Cluster_t_s <- Inv_Cluster_t_s,
Inv_Cluster_t_e <- Inv_Cluster_t_e,
Inv_Cluster_t_nd <- Inv_Cluster_t_nd,
Inv_Cluster_t_s_nd <- Inv_Cluster_t_s_nd,
Inv_Cluster_t_e_nd <- Inv_Cluster_t_e_nd,
Cluster_d <- Cluster_d,
Cluster_d_s <- Cluster_d_s,
Cluster_d_e <- Cluster_d_e,
Cluster_d_nd <- Cluster_d_nd,
Cluster_d_s_nd <- Cluster_d_s_nd,
Cluster_d_e_nd <- Cluster_d_e_nd,
Inv_Cluster_d <- Inv_Cluster_d,
Inv_Cluster_d_s <- Inv_Cluster_d_s,
Inv_Cluster_d_e <- Inv_Cluster_d_e,
Inv_Cluster_d_nd <- Inv_Cluster_d_nd,
Inv_Cluster_d_s_nd <- Inv_Cluster_d_s_nd,
Inv_Cluster_d_e_nd <- Inv_Cluster_d_e_nd,
Average <- Average,
Average_s <- Average_s,
Average_e <- Average_e,
Variances <- Variances,
Variances_s <- Variances_s,
Variances_e <- Variances_e,
Correlation <- Correlation,
Correlation_s <- Correlation_s,
Correlation_e <- Correlation_e,
Sum_average <- Sum_average,
Sum_energy <- Sum_energy,
Sum_entropy <- Sum_entropy,
Sum_variance <- Sum_variance,
Dif_average <- Dif_average,
Dif_energy <- Dif_energy,
Dif_entropy <- Dif_entropy,
Dif_variance <- Dif_variance,
Inv_sum_average <- Inv_sum_average,
Inv_sum_energy <- Inv_sum_energy,
Inv_sum_variance <- Inv_sum_variance,
Inv_dif_average <- Inv_dif_average,
Inv_dif_energy <- Inv_dif_energy,
Inv_dif_variance <- Inv_dif_variance,
IMC1 <- IMC1,
IMC2 <- IMC2,
Energy <- Energy,
Entropy <- Entropy,
Mean <- Mean,
Median <- Median,
Mode <- Mode,
Geo_mean <- Geo_mean,
Geo_mean2 <- Geo_mean2,
Geo_mean3 <- Geo_mean3,
Har_mean <- Har_mean,
Trim_mean_5 <- Trim_mean_5,
Trim_mean_10 <- Trim_mean_10,
Trim_mean_20 <- Trim_mean_20,
IQ_mean <- IQ_mean,
Tri_mean <- Tri_mean,
Mn_AD_mn <- Mn_AD_mn,
Mn_AD_md <- Mn_AD_md,
Md_AD_mn <- Md_AD_mn,
Md_AD_md <- Md_AD_md,
MAD <- MAD,
Max_AD_mn <- Max_AD_mn,
Max_AD_md <- Max_AD_md,
RMS <- RMS,
Min <- Min,
Max <- Max,
Quartiles <- Quartiles,
IQR <- IQR,
Low_notch <- Low_notch,
High_notch <- High_notch,
Range <- Range,
Deciles <- Deciles,
Variance <- Variance,
SD <- SD,
Skew <- Skew,
Kurtosis <- Kurtosis,
Uniformity <- Uniformity
)
stat_names <- c( "Contrast",
"Contrast_s",
"Contrast_e",
"Homogeneity2",
"Homogeneity2_s",
"Homogeneity2_e",
"Homogeneity2_nd",
"Homogeneity2_s_nd",
"Homogeneity2_e_nd",
"Dissimilarity",
"Dissimilarity_s",
"Dissimilarity_e",
"Homogeneity1",
"Homogeneity1_s",
"Homogeneity1_e",
"Homogeneity1_nd",
"Homogeneity1_s_nd",
"Homogeneity1_e_nd",
"DMN",
"DMN_s",
"DMN_e",
"IDMN",
"IDMN_s",
"IDMN_e",
"IDMN_nd",
"IDMN_s_nd",
"IDMN_e_nd",
"DN",
"DN_s",
"DN_e",
"IDN",
"IDN_s",
"IDN_e",
"IDN_nd",
"IDN_s_nd",
"IDN_e_nd",
"Autocorrelation",
"Autocorrelation_s",
"Autocorrelation_e",
"Autocorrelation_nd",
"Autocorrelation_s_nd",
"Autocorrelation_e_nd",
"Inv_autocorrelation",
"Inv_autocorrelation_s",
"Inv_autocorrelation_e",
"Inv_autocorrelation_nd",
"Inv_autocorrelation_s_nd",
"Inv_autocorrelation_e_nd",
"Gauss",
"Gauss_s",
"Gauss_e",
"Gauss_nd",
"Gauss_s_nd",
"Gauss_e_nd",
"Gauss_lp",
"Gauss_lp_s",
"Gauss_lp_e",
"Gauss_lp_nd",
"Gauss_lp_s_nd",
"Gauss_lp_e_nd",
"Gauss_lf",
"Gauss_lf_s",
"Gauss_lf_e",
"Gauss_lf_nd",
"Gauss_lf_s_nd",
"Gauss_lf_e_nd",
"Gauss_rf",
"Gauss_rf_s",
"Gauss_rf_e",
"Gauss_rf_nd",
"Gauss_rf_s_nd",
"Gauss_rf_e_nd",
"Gauss_rp",
"Gauss_rp_s",
"Gauss_rp_e",
"Gauss_rp_nd",
"Gauss_rp_s_nd",
"Gauss_rp_e_nd",
"Inv_Gauss",
"Inv_Gauss_s",
"Inv_Gauss_e",
"Inv_Gauss_nd",
"Inv_Gauss_s_nd",
"Inv_Gauss_e_nd",
"Inv_Gauss_lp",
"Inv_Gauss_lp_s",
"Inv_Gauss_lp_e",
"Inv_Gauss_lp_nd",
"Inv_Gauss_lp_s_nd",
"Inv_Gauss_lp_e_nd",
"Inv_Gauss_lf",
"Inv_Gauss_lf_s",
"Inv_Gauss_lf_e",
"Inv_Gauss_lf_nd",
"Inv_Gauss_lf_s_nd",
"Inv_Gauss_lf_e_nd",
"Inv_Gauss_rf",
"Inv_Gauss_rf_s",
"Inv_Gauss_rf_e",
"Inv_Gauss_rf_nd",
"Inv_Gauss_rf_s_nd",
"Inv_Gauss_rf_e_nd",
"Inv_Gauss_rp",
"Inv_Gauss_rp_s",
"Inv_Gauss_rp_e",
"Inv_Gauss_rp_nd",
"Inv_Gauss_rp_s_nd",
"Inv_Gauss_rp_e_nd",
"Gauss_2f",
"Gauss_2f_s",
"Gauss_2f_e",
"Gauss_2f_nd",
"Gauss_2f_s_nd",
"Gauss_2f_e_nd",
"Inv_Gauss_2f",
"Inv_Gauss_2f_s",
"Inv_Gauss_2f_e",
"Inv_Gauss_2f_nd",
"Inv_Gauss_2f_s_nd",
"Inv_Gauss_2f_e_nd",
"Gauss_2p",
"Gauss_2p_s",
"Gauss_2p_e",
"Gauss_2p_nd",
"Gauss_2p_s_nd",
"Gauss_2p_e_nd",
"Inv_Gauss_2p",
"Inv_Gauss_2p_s",
"Inv_Gauss_2p_e",
"Inv_Gauss_2p_nd",
"Inv_Gauss_2p_s_nd",
"Inv_Gauss_2p_e_nd",
"Cluster_p",
"Cluster_p_s",
"Cluster_p_e",
"Cluster_p_nd",
"Cluster_p_s_nd",
"Cluster_p_e_nd",
"Inv_Cluster_p",
"Inv_Cluster_p_s",
"Inv_Cluster_p_e",
"Inv_Cluster_p_nd",
"Inv_Cluster_p_s_nd",
"Inv_Cluster_p_e_nd",
"Cluster_s",
"Cluster_s_s",
"Cluster_s_e",
"Cluster_s_nd",
"Cluster_s_s_nd",
"Cluster_s_e_nd",
"Inv_Cluster_s",
"Inv_Cluster_s_s",
"Inv_Cluster_s_e",
"Inv_Cluster_s_nd",
"Inv_Cluster_s_s_nd",
"Inv_Cluster_s_e_nd",
"Cluster_t",
"Cluster_t_s",
"Cluster_t_e",
"Cluster_t_nd",
"Cluster_t_s_nd",
"Cluster_t_e_nd",
"Inv_Cluster_t",
"Inv_Cluster_t_s",
"Inv_Cluster_t_e",
"Inv_Cluster_t_nd",
"Inv_Cluster_t_s_nd",
"Inv_Cluster_t_e_nd",
"Cluster_d",
"Cluster_d_s",
"Cluster_d_e",
"Cluster_d_nd",
"Cluster_d_s_nd",
"Cluster_d_e_nd",
"Inv_Cluster_d",
"Inv_Cluster_d_s",
"Inv_Cluster_d_e",
"Inv_Cluster_d_nd",
"Inv_Cluster_d_s_nd",
"Inv_Cluster_d_e_nd",
"Average",
"Average_s",
"Average_e",
"Variances",
"Variances_s",
"Variances_e",
"Correlation",
"Correlation_s",
"Correlation_e",
"Sum_average",
"Sum_energy",
"Sum_entropy",
"Sum_variance",
"Dif_average",
"Dif_energy",
"Dif_entropy",
"Dif_variance",
"Inv_sum_average",
"Inv_sum_energy",
"Inv_sum_variance",
"Inv_dif_average",
"Inv_dif_energy",
"Inv_dif_variance",
"IMC1",
"IMC2",
"Energy",
"Entropy",
"Mean",
"Median",
"Mode",
"Geo_mean",
"Geo_mean2",
"Geo_mean3",
"Har_mean",
"Trim_mean_5",
"Trim_mean_10",
"Trim_mean_20",
"IQ_mean",
"Tri_mean",
"Mn_AD_mn",
"Mn_AD_md",
"Md_AD_mn",
"Md_AD_md",
"MAD",
"Max_AD_mn",
"Max_AD_md",
"RMS",
"Min",
"Max",
"Quartiles",
"IQR",
"Low_notch",
"High_notch",
"Range",
"Deciles",
"Variance",
"SD",
"Skew",
"Kurtosis",
"Uniformity")
names(metrics) <- stat_names
if(use_type == "single") {
if(any(class(RIA_data_in) == "RIA_image"))
{
if(is.null(save_name)) {
txt <- automatic_name(RIA_data_in, use_orig, use_slot)
RIA_data_in$stat_glcm[[txt]] <- metrics
}
if(!is.null(save_name)) {RIA_data_in$stat_glcm[[save_name]] <- metrics
}
}
}
if(use_type == "glcm") {
if(any(class(RIA_data_in) == "RIA_image"))
{
if(is.null(save_name[i])) {
txt <- list_names[i]
RIA_data_in$stat_glcm[[txt]] <- metrics
}
if(!is.null(save_name[i])) {RIA_data_in$stat_glcm[[save_name[i]]] <- metrics
}
}
}
if(is.null(save_name)) {txt_name <- txt
} else {txt_name <- save_name}
if(verbose_in) {message(paste0("GLCM STATISTICS WAS SUCCESSFULLY ADDED TO '", txt_name, "' SLOT OF RIA_image$stat_glcm\n"))}
}
if(any(class(RIA_data_in) == "RIA_image") ) return(RIA_data_in)
else return(metrics)
}
neuroconductor/RIA documentation built on May 21, 2021, 6:56 a.m.
R Package Documentation
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Defines functions glcm_stat
Documented in glcm_stat
#' @title Calculates GLCM-based statistics
#' @export
#'
#' @description Calculates GLCM-based statistics for given GLCM matrix.
#'
#' @param RIA_data_in \emph{RIA_image}.
#'
#' @param use_type string, can be \emph{"single"} which runs the function on a single image,
#' which is determined using \emph{"use_orig"} or \emph{"use_slot"}. \emph{"glcm"}
#' takes all datasets in the \emph{RIA_image$glcm} slot and runs the analysis on them.
#'
#' @param use_orig logical, indicating to use image present in \emph{RIA_data$orig}.
#' If FALSE, the modified image will be used stored in \emph{RIA_data$modif}. However, GLCM matrices
#' are usually not present in either slots, therefore giving the slot name using \emph{use_slot} is advised.
#'
#' @param use_slot string, name of slot where data wished to be used is. Use if the desired image
#' is not in the \emph{data$orig} or \emph{data$modif} slot of the \emph{RIA_image}. For example,
#' ig the desired dataset is in \emph{RIA_image$glcm$ep_4_111}, then \emph{use_slot} should be
#' \emph{glcm$ep_4_111}. The results are automatically saved. If the results are not saved to
#' the desired slot, then please use \emph{save_name} parameter. If the string contains "-" characters
#' use "`" before the last slot name, for example: \emph{glcm$`ep_4_-1-1-1`}
#'
#' @param save_name string, indicating the name of subslot of \emph{$glcm} to save results to.
#' If left empty, then it will be automatically determined.
#'
#' @param verbose_in logical, indicating whether to print detailed information.
#' Most prints can also be suppressed using the \code{\link{suppressMessages}} function.
#'
#' @return \emph{RIA_image} containing the statistical information.
#'
#' @examples \dontrun{
#' #Discretize loaded image and then calculate GLCM statistics
#' RIA_image <- discretize(RIA_image, bins_in = 8, equal_prob = TRUE)
#' RIA_image <- glcm(RIA_image, use_orig = FALSE, use_slot = "discretized$ep_8",
#' off_right = 0, off_down = 1, off_z = 0)
#' RIA_image <- glcm_stat(RIA_image, use_orig = FALSE, use_slot = "glcm$ep_8_010")
#'
#' #Batch calculation of GLCM-based statistics on all calculated GLCMs
#' RIA_image <- glcm_stat(RIA_image, use_type = "discretized")
#' }
#'
#' @references Márton KOLOSSVÁRY et al.
#' Radiomic Features Are Superior to Conventional Quantitative Computed Tomographic
#' Metrics to Identify Coronary Plaques With Napkin-Ring Sign
#' Circulation: Cardiovascular Imaging (2017).
#' DOI: 10.1161/circimaging.117.006843
#' \url{https://www.ncbi.nlm.nih.gov/pubmed/29233836}
#'
#' Márton KOLOSSVÁRY et al.
#' Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques.
#' Journal of Thoracic Imaging (2018).
#' DOI: 10.1097/RTI.0000000000000268
#' \url{https://www.ncbi.nlm.nih.gov/pubmed/28346329}
#' @encoding UTF-8
glcm_stat <- function(RIA_data_in, use_type = "single", use_orig = FALSE, use_slot = "glcm$es_8_111", save_name = NULL, verbose_in = TRUE)
{
data_in_orig <- check_data_in(RIA_data_in, use_type = use_type, use_slot = use_slot, verbose_in = verbose_in)
if(any(class(data_in_orig) != "list")) data_in_orig <- list(data_in_orig)
list_names <- names(data_in_orig)
if(!is.null(save_name) & (length(data_in_orig) != length(save_name))) {stop(paste0("PLEASE PROVIDE THE SAME NUMBER OF NAMES AS THERE ARE IMAGES!\n",
"NUMBER OF NAMES: ", length(save_name), "\n",
"NUMBER OF IMAGES: ", length(data_in), "\n"))
}
for (i in 1: length(data_in_orig))
{
data_in <- data_in_orig[[i]]
data_NA <- as.vector(data_in)
data_NA <- data_NA[!is.na(data_NA)]
if(length(data_NA) == 0) {message("WARNING: SUPPLIED RIA_image DOES NOT CONTAIN ANY DATA!!!")}
Contrast <- contrast(data_in)
Contrast_s <- contrast(data_in, type_in = "squared")
Contrast_e <- contrast(data_in, type_in = "entropy")
Homogeneity2 <- homogeneity2(data_in)
Homogeneity2_s <- homogeneity2(data_in, type_in = "squared")
Homogeneity2_e <- homogeneity2(data_in, type_in = "entropy")
Homogeneity2_nd <- homogeneity2(data_in, diag = FALSE)
Homogeneity2_s_nd <- homogeneity2(data_in, type_in = "squared", diag = FALSE)
Homogeneity2_e_nd <- homogeneity2(data_in, type_in = "entropy", diag = FALSE)
Dissimilarity <- dissimilarity(data_in)
Dissimilarity_s <- dissimilarity(data_in, type_in = "squared")
Dissimilarity_e <- dissimilarity(data_in, type_in = "entropy")
Homogeneity1 <- homogeneity1(data_in)
Homogeneity1_s <- homogeneity1(data_in, type_in = "squared")
Homogeneity1_e <- homogeneity1(data_in, type_in = "entropy")
Homogeneity1_nd <- homogeneity1(data_in, diag = FALSE)
Homogeneity1_s_nd <- homogeneity1(data_in, type_in = "squared", diag = FALSE)
Homogeneity1_e_nd <- homogeneity1(data_in, type_in = "entropy", diag = FALSE)
DMN <- dmn(data_in)
DMN_s <- dmn(data_in, type_in = "squared")
DMN_e <- dmn(data_in, type_in = "entropy")
IDMN <- idmn(data_in)
IDMN_s <- idmn(data_in, type_in = "squared")
IDMN_e <- idmn(data_in, type_in = "entropy")
IDMN_nd <- idmn(data_in, diag = FALSE)
IDMN_s_nd <- idmn(data_in, type_in = "squared", diag = FALSE)
IDMN_e_nd <- idmn(data_in, type_in = "entropy", diag = FALSE)
DN <- dn(data_in)
DN_s <- dn(data_in, type_in = "squared")
DN_e <- dn(data_in, type_in = "entropy")
IDN <- idn(data_in)
IDN_s <- idn(data_in, type_in = "squared")
IDN_e <- idn(data_in, type_in = "entropy")
IDN_nd <- idn(data_in, diag = FALSE)
IDN_s_nd <- idn(data_in, type_in = "squared", diag = FALSE)
IDN_e_nd <- idn(data_in, type_in = "entropy", diag = FALSE)
Autocorrelation <- autocorrelation(data_in)
Autocorrelation_s <- autocorrelation(data_in, type_in = "squared")
Autocorrelation_e <- autocorrelation(data_in, type_in = "entropy")
Autocorrelation_nd <- autocorrelation(data_in, diag = FALSE)
Autocorrelation_s_nd <- autocorrelation(data_in, type_in = "squared", diag = FALSE)
Autocorrelation_e_nd <- autocorrelation(data_in, type_in = "entropy", diag = FALSE)
Inv_autocorrelation <- inv_autocorrelation(data_in)
Inv_autocorrelation_s <- inv_autocorrelation(data_in, type_in = "squared")
Inv_autocorrelation_e <- inv_autocorrelation(data_in, type_in = "entropy")
Inv_autocorrelation_nd <- inv_autocorrelation(data_in, diag = FALSE)
Inv_autocorrelation_s_nd <- inv_autocorrelation(data_in, type_in = "squared", diag = FALSE)
Inv_autocorrelation_e_nd <- inv_autocorrelation(data_in, type_in = "entropy", diag = FALSE)
Gauss <- gauss(data_in)
Gauss_s <- gauss(data_in, type_in = "squared")
Gauss_e <- gauss(data_in, type_in = "entropy")
Gauss_nd <- gauss(data_in, diag = FALSE)
Gauss_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE)
Gauss_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE)
Gauss_lp <- gauss(data_in, loc = 1)
Gauss_lp_s <- gauss(data_in, type_in = "squared", loc = 1)
Gauss_lp_e <- gauss(data_in, type_in = "entropy", loc = 1)
Gauss_lp_nd <- gauss(data_in, diag = FALSE, loc = 1)
Gauss_lp_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 1)
Gauss_lp_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 1)
Gauss_lf <- gauss(data_in, loc = 2)
Gauss_lf_s <- gauss(data_in, type_in = "squared", loc = 2)
Gauss_lf_e <- gauss(data_in, type_in = "entropy", loc = 2)
Gauss_lf_nd <- gauss(data_in, diag = FALSE, loc = 2)
Gauss_lf_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 2)
Gauss_lf_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 2)
Gauss_rf <- gauss(data_in, loc = 4)
Gauss_rf_s <- gauss(data_in, type_in = "squared", loc = 4)
Gauss_rf_e <- gauss(data_in, type_in = "entropy", loc = 4)
Gauss_rf_nd <- gauss(data_in, diag = FALSE, loc = 4)
Gauss_rf_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 4)
Gauss_rf_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 4)
Gauss_rp <- gauss(data_in, loc = 5)
Gauss_rp_s <- gauss(data_in, type_in = "squared", loc = 5)
Gauss_rp_e <- gauss(data_in, type_in = "entropy", loc = 5)
Gauss_rp_nd <- gauss(data_in, diag = FALSE, loc = 5)
Gauss_rp_s_nd <- gauss(data_in, type_in = "squared", diag = FALSE, loc = 5)
Gauss_rp_e_nd <- gauss(data_in, type_in = "entropy", diag = FALSE, loc = 5)
Inv_Gauss <- gauss(data_in, inverse = TRUE)
Inv_Gauss_s <- gauss(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_e <- gauss(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_nd <- gauss(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Inv_Gauss_lp <- gauss(data_in, inverse = TRUE, loc = 1)
Inv_Gauss_lp_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 1)
Inv_Gauss_lp_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 1)
Inv_Gauss_lp_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 1)
Inv_Gauss_lp_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 1)
Inv_Gauss_lp_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 1)
Inv_Gauss_lf <- gauss(data_in, inverse = TRUE, loc = 2)
Inv_Gauss_lf_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 2)
Inv_Gauss_lf_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 2)
Inv_Gauss_lf_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 2)
Inv_Gauss_lf_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 2)
Inv_Gauss_lf_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 2)
Inv_Gauss_rf <- gauss(data_in, inverse = TRUE, loc = 4)
Inv_Gauss_rf_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 4)
Inv_Gauss_rf_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 4)
Inv_Gauss_rf_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 4)
Inv_Gauss_rf_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 4)
Inv_Gauss_rf_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 4)
Inv_Gauss_rp <- gauss(data_in, inverse = TRUE, loc = 5)
Inv_Gauss_rp_s <- gauss(data_in, inverse = TRUE, type_in = "squared", loc = 5)
Inv_Gauss_rp_e <- gauss(data_in, inverse = TRUE, type_in = "entropy", loc = 5)
Inv_Gauss_rp_nd <- gauss(data_in, inverse = TRUE, diag = FALSE, loc = 5)
Inv_Gauss_rp_s_nd <- gauss(data_in, inverse = TRUE, type_in = "squared", diag = FALSE, loc = 5)
Inv_Gauss_rp_e_nd <- gauss(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE, loc = 5)
Gauss_2f <- gauss2f(data_in)
Gauss_2f_s <- gauss2f(data_in, type_in = "squared")
Gauss_2f_e <- gauss2f(data_in, type_in = "entropy")
Gauss_2f_nd <- gauss2f(data_in, diag = FALSE)
Gauss_2f_s_nd <- gauss2f(data_in, type_in = "squared", diag = FALSE)
Gauss_2f_e_nd <- gauss2f(data_in, type_in = "entropy", diag = FALSE)
Inv_Gauss_2f <- gauss2f(data_in, inverse = TRUE)
Inv_Gauss_2f_s <- gauss2f(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_2f_e <- gauss2f(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_2f_nd <- gauss2f(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_2f_s_nd <- gauss2f(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_2f_e_nd <- gauss2f(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Gauss_2p <- gauss2p(data_in)
Gauss_2p_s <- gauss2p(data_in, type_in = "squared")
Gauss_2p_e <- gauss2p(data_in, type_in = "entropy")
Gauss_2p_nd <- gauss2p(data_in, diag = FALSE)
Gauss_2p_s_nd <- gauss2p(data_in, type_in = "squared", diag = FALSE)
Gauss_2p_e_nd <- gauss2p(data_in, type_in = "entropy", diag = FALSE)
Inv_Gauss_2p <- gauss2p(data_in, inverse = TRUE)
Inv_Gauss_2p_s <- gauss2p(data_in, inverse = TRUE, type_in = "squared")
Inv_Gauss_2p_e <- gauss2p(data_in, inverse = TRUE, type_in = "entropy")
Inv_Gauss_2p_nd <- gauss2p(data_in, inverse = TRUE, diag = FALSE)
Inv_Gauss_2p_s_nd <- gauss2p(data_in, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Gauss_2p_e_nd <- gauss2p(data_in, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_p <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_p_s <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_p_e <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_p_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_p_s_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_p_e_nd <- cluster(data_in, pow = 4, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_p <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_p_s <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_p_e <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_p_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_p_s_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_p_e_nd <- cluster(data_in, pow = 4, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_s <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_s_s <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_s_e <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_s_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_s_s_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_s_e_nd <- cluster(data_in, pow = 3, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_s <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_s_s <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_s_e <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_s_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_s_s_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_s_e_nd <- cluster(data_in, pow = 3, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_t <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_t_s <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_t_e <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_t_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_t_s_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_t_e_nd <- cluster(data_in, pow = 2, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_t <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_t_s <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_t_e <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_t_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_t_s_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_t_e_nd <- cluster(data_in, pow = 2, inverse = TRUE, type_in = "entropy", diag = FALSE)
Cluster_d <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "single", diag = TRUE)
Cluster_d_s <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "squared", diag = TRUE)
Cluster_d_e <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "entropy", diag = TRUE)
Cluster_d_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "single", diag = FALSE)
Cluster_d_s_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "squared", diag = FALSE)
Cluster_d_e_nd <- cluster(data_in, pow = 1, inverse = FALSE, type_in = "entropy", diag = FALSE)
Inv_Cluster_d <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "single", diag = TRUE)
Inv_Cluster_d_s <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "squared", diag = TRUE)
Inv_Cluster_d_e <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "entropy", diag = TRUE)
Inv_Cluster_d_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "single", diag = FALSE)
Inv_Cluster_d_s_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "squared", diag = FALSE)
Inv_Cluster_d_e_nd <- cluster(data_in, pow = 1, inverse = TRUE, type_in = "entropy", diag = FALSE)
Average <- avg(data_in, type_in = "single")
Average_s <- avg(data_in, type_in = "squared")
Average_e <- avg(data_in, type_in = "entropy")
Variances <- variance(data_in, type_in = "single")
Variances_s <- variance(data_in, type_in = "squared")
Variances_e <- variance(data_in, type_in = "entropy")
Correlation <- correlation(data_in, type_in = "single")
Correlation_s <- correlation(data_in, type_in = "squared")
Correlation_e <- correlation(data_in, type_in = "entropy")
Sum_average <- sum_f(data_in, type_in = "single")
Sum_energy <- sum_f(data_in, type_in = "squared")
Sum_entropy <- sum_f(data_in, type_in = "entropy")
Sum_variance <- sum_f(data_in, type_in = "variance")
Dif_average <- dif_f(data_in, type_in = "single")
Dif_energy <- dif_f(data_in, type_in = "squared")
Dif_entropy <- dif_f(data_in, type_in = "entropy")
Dif_variance <- dif_f(data_in, type_in = "variance")
Inv_sum_average <- sum_f(data_in, inverse = TRUE, type_in = "single")
Inv_sum_energy <- sum_f(data_in, inverse = TRUE, type_in = "squared")
Inv_sum_variance <- sum_f(data_in, inverse = TRUE, type_in = "variance")
Inv_dif_average <- dif_f(data_in, inverse = TRUE, type_in = "single")
Inv_dif_energy <- dif_f(data_in, inverse = TRUE, type_in = "squared")
Inv_dif_variance <- dif_f(data_in, inverse = TRUE, type_in = "variance")
IMC1 <- imc1(data_in)
IMC2 <- imc2(data_in)
data <- as.vector(data_in)
data <- data[!is.na(data)]
Energy <- energy(data)
Entropy <- entropy(data, 2)
Mean <- base::mean(data)
Median <- stats::median(data)
Mode <- mode(data)[1]
Geo_mean <- geo_mean(data)
Geo_mean2 <- geo_mean2(data)
Geo_mean3 <- geo_mean3(data)
Har_mean <- har_mean(data)
Trim_mean_5 <- base::mean(data, trim = 0.025)
Trim_mean_10 <- base::mean(data, trim = 0.05)
Trim_mean_20 <- base::mean(data, trim = 0.1)
IQ_mean <- base::mean(data, trim = 0.25)
Tri_mean <- (as.numeric(stats::quantile(data, 0.25)) +2*as.numeric(stats::quantile(data, 0.50)) + as.numeric(stats::quantile(data, 0.25)))/4
Mn_AD_mn <- mn_AD_mn(data)
Mn_AD_md <- mn_AD_md(data)
Md_AD_mn <- md_AD_mn(data)
Md_AD_md <- md_AD_md(data)
MAD <- stats::mad(data)
Max_AD_mn <- max_AD_mn(data)
Max_AD_md <- max_AD_md(data)
RMS <- rms(data)
Min <- base::min(data)
Max <- base::max(data)
Quartiles <- stats::quantile(data, seq(0.25, 0.75, 0.50))
IQR <- stats::IQR(data)
Low_notch <- as.numeric(Quartiles[1])-1.5*IQR
High_notch <- as.numeric(Quartiles[1])+1.5*IQR
Range <- abs(abs(base::range(data)[2] - base::range(data)[1]))
Deciles <- stats::quantile(data, seq(0.1, 0.9, 0.1))
Variance <- ifelse(length(data)>1, stats::var(data), 0)
SD <- ifelse(length(data)>1, stats::sd(data), 0)
Skew <- ifelse(length(data)>1, skew(data), 0)
Kurtosis <- ifelse(length(data)>1, kurtosis(data), 0)
Uniformity <- uniformity(data)
metrics <- list(
Contrast <- Contrast,
Contrast_s <- Contrast_s,
Contrast_e <- Contrast_e,
Homogeneity2 <- Homogeneity2,
Homogeneity2_s <- Homogeneity2_s,
Homogeneity2_e <- Homogeneity2_e,
Homogeneity2_nd <- Homogeneity2_nd,
Homogeneity2_s_nd <- Homogeneity2_s_nd,
Homogeneity2_e_nd <- Homogeneity2_e_nd,
Dissimilarity <- Dissimilarity,
Dissimilarity_s <- Dissimilarity_s,
Dissimilarity_e <- Dissimilarity_e,
Homogeneity1 <- Homogeneity1,
Homogeneity1_s <- Homogeneity1_s,
Homogeneity1_e <- Homogeneity1_e,
Homogeneity1_nd <- Homogeneity1_nd,
Homogeneity1_s_nd <- Homogeneity1_s_nd,
Homogeneity1_e_nd <- Homogeneity1_e_nd,
DMN <- DMN,
DMN_s <- DMN_s,
DMN_e <- DMN_e,
IDMN <- IDMN,
IDMN_s <- IDMN_s,
IDMN_e <- IDMN_e,
IDMN_nd <- IDMN_nd,
IDMN_s_nd <- IDMN_s_nd,
IDMN_e_nd <- IDMN_e_nd,
DN <- DN,
DN_s <- DN_s,
DN_e <- DN_e,
IDN <- IDN,
IDN_s <- IDN_s,
IDN_e <- IDN_e,
IDN_nd <- IDN_nd,
IDN_s_nd <- IDN_s_nd,
IDN_e_nd <- IDN_e_nd,
Autocorrelation <- Autocorrelation,
Autocorrelation_s <- Autocorrelation_s,
Autocorrelation_e <- Autocorrelation_e,
Autocorrelation_nd <- Autocorrelation_nd,
Autocorrelation_s_nd <- Autocorrelation_s_nd,
Autocorrelation_e_nd <- Autocorrelation_e_nd,
Inv_autocorrelation <- Inv_autocorrelation,
Inv_autocorrelation_s <- Inv_autocorrelation_s,
Inv_autocorrelation_e <- Inv_autocorrelation_e,
Inv_autocorrelation_nd <- Inv_autocorrelation_nd,
Inv_autocorrelation_s_nd <- Inv_autocorrelation_s_nd,
Inv_autocorrelation_e_nd <- Inv_autocorrelation_e_nd,
Gauss <- Gauss,
Gauss_s <- Gauss_s,
Gauss_e <- Gauss_e,
Gauss_nd <- Gauss_nd,
Gauss_s_nd <- Gauss_s_nd,
Gauss_e_nd <- Gauss_e_nd,
Gauss_lp <- Gauss_lp,
Gauss_lp_s <- Gauss_lp_s,
Gauss_lp_e <- Gauss_lp_e,
Gauss_lp_nd <- Gauss_lp_nd,
Gauss_lp_s_nd <- Gauss_lp_s_nd,
Gauss_lp_e_nd <- Gauss_lp_e_nd,
Gauss_lf <- Gauss_lf,
Gauss_lf_s <- Gauss_lf_s,
Gauss_lf_e <- Gauss_lf_e,
Gauss_lf_nd <- Gauss_lf_nd,
Gauss_lf_s_nd <- Gauss_lf_s_nd,
Gauss_lf_e_nd <- Gauss_lf_e_nd,
Gauss_rf <- Gauss_rf,
Gauss_rf_s <- Gauss_rf_s,
Gauss_rf_e <- Gauss_rf_e,
Gauss_rf_nd <- Gauss_rf_nd,
Gauss_rf_s_nd <- Gauss_rf_s_nd,
Gauss_rf_e_nd <- Gauss_rf_e_nd,
Gauss_rp <- Gauss_rp,
Gauss_rp_s <- Gauss_rp_s,
Gauss_rp_e <- Gauss_rp_e,
Gauss_rp_nd <- Gauss_rp_nd,
Gauss_rp_s_nd <- Gauss_rp_s_nd,
Gauss_rp_e_nd <- Gauss_rp_e_nd,
Inv_Gauss <- Inv_Gauss,
Inv_Gauss_s <- Inv_Gauss_s,
Inv_Gauss_e <- Inv_Gauss_e,
Inv_Gauss_nd <- Inv_Gauss_nd,
Inv_Gauss_s_nd <- Inv_Gauss_s_nd,
Inv_Gauss_e_nd <- Inv_Gauss_e_nd,
Inv_Gauss_lp <- Inv_Gauss_lp,
Inv_Gauss_lp_s <- Inv_Gauss_lp_s,
Inv_Gauss_lp_e <- Inv_Gauss_lp_e,
Inv_Gauss_lp_nd <- Inv_Gauss_lp_nd,
Inv_Gauss_lp_s_nd <- Inv_Gauss_lp_s_nd,
Inv_Gauss_lp_e_nd <- Inv_Gauss_lp_e_nd,
Inv_Gauss_lf <- Inv_Gauss_lf,
Inv_Gauss_lf_s <- Inv_Gauss_lf_s,
Inv_Gauss_lf_e <- Inv_Gauss_lf_e,
Inv_Gauss_lf_nd <- Inv_Gauss_lf_nd,
Inv_Gauss_lf_s_nd <- Inv_Gauss_lf_s_nd,
Inv_Gauss_lf_e_nd <- Inv_Gauss_lf_e_nd,
Inv_Gauss_rf <- Inv_Gauss_rf,
Inv_Gauss_rf_s <- Inv_Gauss_rf_s,
Inv_Gauss_rf_e <- Inv_Gauss_rf_e,
Inv_Gauss_rf_nd <- Inv_Gauss_rf_nd,
Inv_Gauss_rf_s_nd <- Inv_Gauss_rf_s_nd,
Inv_Gauss_rf_e_nd <- Inv_Gauss_rf_e_nd,
Inv_Gauss_rp <- Inv_Gauss_rp,
Inv_Gauss_rp_s <- Inv_Gauss_rp_s,
Inv_Gauss_rp_e <- Inv_Gauss_rp_e,
Inv_Gauss_rp_nd <- Inv_Gauss_rp_nd,
Inv_Gauss_rp_s_nd <- Inv_Gauss_rp_s_nd,
Inv_Gauss_rp_e_nd <- Inv_Gauss_rp_e_nd,
Gauss_2f <- Gauss_2f,
Gauss_2f_s <- Gauss_2f_s,
Gauss_2f_e <- Gauss_2f_e,
Gauss_2f_nd <- Gauss_2f_nd,
Gauss_2f_s_nd <- Gauss_2f_s_nd,
Gauss_2f_e_nd <- Gauss_2f_e_nd,
Inv_Gauss_2f <- Inv_Gauss_2f,
Inv_Gauss_2f_s <- Inv_Gauss_2f_s,
Inv_Gauss_2f_e <- Inv_Gauss_2f_e,
Inv_Gauss_2f_nd <- Inv_Gauss_2f_nd,
Inv_Gauss_2f_s_nd <- Inv_Gauss_2f_s_nd,
Inv_Gauss_2f_e_nd <- Inv_Gauss_2f_e_nd,
Gauss_2p <- Gauss_2p,
Gauss_2p_s <- Gauss_2p_s,
Gauss_2p_e <- Gauss_2p_e,
Gauss_2p_nd <- Gauss_2p_nd,
Gauss_2p_s_nd <- Gauss_2p_s_nd,
Gauss_2p_e_nd <- Gauss_2p_e_nd,
Inv_Gauss_2p <- Inv_Gauss_2p,
Inv_Gauss_2p_s <- Inv_Gauss_2p_s,
Inv_Gauss_2p_e <- Inv_Gauss_2p_e,
Inv_Gauss_2p_nd <- Inv_Gauss_2p_nd,
Inv_Gauss_2p_s_nd <- Inv_Gauss_2p_s_nd,
Inv_Gauss_2p_e_nd <- Inv_Gauss_2p_e_nd,
Cluster_p <- Cluster_p,
Cluster_p_s <- Cluster_p_s,
Cluster_p_e <- Cluster_p_e,
Cluster_p_nd <- Cluster_p_nd,
Cluster_p_s_nd <- Cluster_p_s_nd,
Cluster_p_e_nd <- Cluster_p_e_nd,
Inv_Cluster_p <- Inv_Cluster_p,
Inv_Cluster_p_s <- Inv_Cluster_p_s,
Inv_Cluster_p_e <- Inv_Cluster_p_e,
Inv_Cluster_p_nd <- Inv_Cluster_p_nd,
Inv_Cluster_p_s_nd <- Inv_Cluster_p_s_nd,
Inv_Cluster_p_e_nd <- Inv_Cluster_p_e_nd,
Cluster_s <- Cluster_s,
Cluster_s_s <- Cluster_s_s,
Cluster_s_e <- Cluster_s_e,
Cluster_s_nd <- Cluster_s_nd,
Cluster_s_s_nd <- Cluster_s_s_nd,
Cluster_s_e_nd <- Cluster_s_e_nd,
Inv_Cluster_s <- Inv_Cluster_s,
Inv_Cluster_s_s <- Inv_Cluster_s_s,
Inv_Cluster_s_e <- Inv_Cluster_s_e,
Inv_Cluster_s_nd <- Inv_Cluster_s_nd,
Inv_Cluster_s_s_nd <- Inv_Cluster_s_s_nd,
Inv_Cluster_s_e_nd <- Inv_Cluster_s_e_nd,
Cluster_t <- Cluster_t,
Cluster_t_s <- Cluster_t_s,
Cluster_t_e <- Cluster_t_e,
Cluster_t_nd <- Cluster_t_nd,
Cluster_t_s_nd <- Cluster_t_s_nd,
Cluster_t_e_nd <- Cluster_t_e_nd,
Inv_Cluster_t <- Inv_Cluster_t,
Inv_Cluster_t_s <- Inv_Cluster_t_s,
Inv_Cluster_t_e <- Inv_Cluster_t_e,
Inv_Cluster_t_nd <- Inv_Cluster_t_nd,
Inv_Cluster_t_s_nd <- Inv_Cluster_t_s_nd,
Inv_Cluster_t_e_nd <- Inv_Cluster_t_e_nd,
Cluster_d <- Cluster_d,
Cluster_d_s <- Cluster_d_s,
Cluster_d_e <- Cluster_d_e,
Cluster_d_nd <- Cluster_d_nd,
Cluster_d_s_nd <- Cluster_d_s_nd,
Cluster_d_e_nd <- Cluster_d_e_nd,
Inv_Cluster_d <- Inv_Cluster_d,
Inv_Cluster_d_s <- Inv_Cluster_d_s,
Inv_Cluster_d_e <- Inv_Cluster_d_e,
Inv_Cluster_d_nd <- Inv_Cluster_d_nd,
Inv_Cluster_d_s_nd <- Inv_Cluster_d_s_nd,
Inv_Cluster_d_e_nd <- Inv_Cluster_d_e_nd,
Average <- Average,
Average_s <- Average_s,
Average_e <- Average_e,
Variances <- Variances,
Variances_s <- Variances_s,
Variances_e <- Variances_e,
Correlation <- Correlation,
Correlation_s <- Correlation_s,
Correlation_e <- Correlation_e,
Sum_average <- Sum_average,
Sum_energy <- Sum_energy,
Sum_entropy <- Sum_entropy,
Sum_variance <- Sum_variance,
Dif_average <- Dif_average,
Dif_energy <- Dif_energy,
Dif_entropy <- Dif_entropy,
Dif_variance <- Dif_variance,
Inv_sum_average <- Inv_sum_average,
Inv_sum_energy <- Inv_sum_energy,
Inv_sum_variance <- Inv_sum_variance,
Inv_dif_average <- Inv_dif_average,
Inv_dif_energy <- Inv_dif_energy,
Inv_dif_variance <- Inv_dif_variance,
IMC1 <- IMC1,
IMC2 <- IMC2,
Energy <- Energy,
Entropy <- Entropy,
Mean <- Mean,
Median <- Median,
Mode <- Mode,
Geo_mean <- Geo_mean,
Geo_mean2 <- Geo_mean2,
Geo_mean3 <- Geo_mean3,
Har_mean <- Har_mean,
Trim_mean_5 <- Trim_mean_5,
Trim_mean_10 <- Trim_mean_10,
Trim_mean_20 <- Trim_mean_20,
IQ_mean <- IQ_mean,
Tri_mean <- Tri_mean,
Mn_AD_mn <- Mn_AD_mn,
Mn_AD_md <- Mn_AD_md,
Md_AD_mn <- Md_AD_mn,
Md_AD_md <- Md_AD_md,
MAD <- MAD,
Max_AD_mn <- Max_AD_mn,
Max_AD_md <- Max_AD_md,
RMS <- RMS,
Min <- Min,
Max <- Max,
Quartiles <- Quartiles,
IQR <- IQR,
Low_notch <- Low_notch,
High_notch <- High_notch,
Range <- Range,
Deciles <- Deciles,
Variance <- Variance,
SD <- SD,
Skew <- Skew,
Kurtosis <- Kurtosis,
Uniformity <- Uniformity
)
stat_names <- c( "Contrast",
"Contrast_s",
"Contrast_e",
"Homogeneity2",
"Homogeneity2_s",
"Homogeneity2_e",
"Homogeneity2_nd",
"Homogeneity2_s_nd",
"Homogeneity2_e_nd",
"Dissimilarity",
"Dissimilarity_s",
"Dissimilarity_e",
"Homogeneity1",
"Homogeneity1_s",
"Homogeneity1_e",
"Homogeneity1_nd",
"Homogeneity1_s_nd",
"Homogeneity1_e_nd",
"DMN",
"DMN_s",
"DMN_e",
"IDMN",
"IDMN_s",
"IDMN_e",
"IDMN_nd",
"IDMN_s_nd",
"IDMN_e_nd",
"DN",
"DN_s",
"DN_e",
"IDN",
"IDN_s",
"IDN_e",
"IDN_nd",
"IDN_s_nd",
"IDN_e_nd",
"Autocorrelation",
"Autocorrelation_s",
"Autocorrelation_e",
"Autocorrelation_nd",
"Autocorrelation_s_nd",
"Autocorrelation_e_nd",
"Inv_autocorrelation",
"Inv_autocorrelation_s",
"Inv_autocorrelation_e",
"Inv_autocorrelation_nd",
"Inv_autocorrelation_s_nd",
"Inv_autocorrelation_e_nd",
"Gauss",
"Gauss_s",
"Gauss_e",
"Gauss_nd",
"Gauss_s_nd",
"Gauss_e_nd",
"Gauss_lp",
"Gauss_lp_s",
"Gauss_lp_e",
"Gauss_lp_nd",
"Gauss_lp_s_nd",
"Gauss_lp_e_nd",
"Gauss_lf",
"Gauss_lf_s",
"Gauss_lf_e",
"Gauss_lf_nd",
"Gauss_lf_s_nd",
"Gauss_lf_e_nd",
"Gauss_rf",
"Gauss_rf_s",
"Gauss_rf_e",
"Gauss_rf_nd",
"Gauss_rf_s_nd",
"Gauss_rf_e_nd",
"Gauss_rp",
"Gauss_rp_s",
"Gauss_rp_e",
"Gauss_rp_nd",
"Gauss_rp_s_nd",
"Gauss_rp_e_nd",
"Inv_Gauss",
"Inv_Gauss_s",
"Inv_Gauss_e",
"Inv_Gauss_nd",
"Inv_Gauss_s_nd",
"Inv_Gauss_e_nd",
"Inv_Gauss_lp",
"Inv_Gauss_lp_s",
"Inv_Gauss_lp_e",
"Inv_Gauss_lp_nd",
"Inv_Gauss_lp_s_nd",
"Inv_Gauss_lp_e_nd",
"Inv_Gauss_lf",
"Inv_Gauss_lf_s",
"Inv_Gauss_lf_e",
"Inv_Gauss_lf_nd",
"Inv_Gauss_lf_s_nd",
"Inv_Gauss_lf_e_nd",
"Inv_Gauss_rf",
"Inv_Gauss_rf_s",
"Inv_Gauss_rf_e",
"Inv_Gauss_rf_nd",
"Inv_Gauss_rf_s_nd",
"Inv_Gauss_rf_e_nd",
"Inv_Gauss_rp",
"Inv_Gauss_rp_s",
"Inv_Gauss_rp_e",
"Inv_Gauss_rp_nd",
"Inv_Gauss_rp_s_nd",
"Inv_Gauss_rp_e_nd",
"Gauss_2f",
"Gauss_2f_s",
"Gauss_2f_e",
"Gauss_2f_nd",
"Gauss_2f_s_nd",
"Gauss_2f_e_nd",
"Inv_Gauss_2f",
"Inv_Gauss_2f_s",
"Inv_Gauss_2f_e",
"Inv_Gauss_2f_nd",
"Inv_Gauss_2f_s_nd",
"Inv_Gauss_2f_e_nd",
"Gauss_2p",
"Gauss_2p_s",
"Gauss_2p_e",
"Gauss_2p_nd",
"Gauss_2p_s_nd",
"Gauss_2p_e_nd",
"Inv_Gauss_2p",
"Inv_Gauss_2p_s",
"Inv_Gauss_2p_e",
"Inv_Gauss_2p_nd",
"Inv_Gauss_2p_s_nd",
"Inv_Gauss_2p_e_nd",
"Cluster_p",
"Cluster_p_s",
"Cluster_p_e",
"Cluster_p_nd",
"Cluster_p_s_nd",
"Cluster_p_e_nd",
"Inv_Cluster_p",
"Inv_Cluster_p_s",
"Inv_Cluster_p_e",
"Inv_Cluster_p_nd",
"Inv_Cluster_p_s_nd",
"Inv_Cluster_p_e_nd",
"Cluster_s",
"Cluster_s_s",
"Cluster_s_e",
"Cluster_s_nd",
"Cluster_s_s_nd",
"Cluster_s_e_nd",
"Inv_Cluster_s",
"Inv_Cluster_s_s",
"Inv_Cluster_s_e",
"Inv_Cluster_s_nd",
"Inv_Cluster_s_s_nd",
"Inv_Cluster_s_e_nd",
"Cluster_t",
"Cluster_t_s",
"Cluster_t_e",
"Cluster_t_nd",
"Cluster_t_s_nd",
"Cluster_t_e_nd",
"Inv_Cluster_t",
"Inv_Cluster_t_s",
"Inv_Cluster_t_e",
"Inv_Cluster_t_nd",
"Inv_Cluster_t_s_nd",
"Inv_Cluster_t_e_nd",
"Cluster_d",
"Cluster_d_s",
"Cluster_d_e",
"Cluster_d_nd",
"Cluster_d_s_nd",
"Cluster_d_e_nd",
"Inv_Cluster_d",
"Inv_Cluster_d_s",
"Inv_Cluster_d_e",
"Inv_Cluster_d_nd",
"Inv_Cluster_d_s_nd",
"Inv_Cluster_d_e_nd",
"Average",
"Average_s",
"Average_e",
"Variances",
"Variances_s",
"Variances_e",
"Correlation",
"Correlation_s",
"Correlation_e",
"Sum_average",
"Sum_energy",
"Sum_entropy",
"Sum_variance",
"Dif_average",
"Dif_energy",
"Dif_entropy",
"Dif_variance",
"Inv_sum_average",
"Inv_sum_energy",
"Inv_sum_variance",
"Inv_dif_average",
"Inv_dif_energy",
"Inv_dif_variance",
"IMC1",
"IMC2",
"Energy",
"Entropy",
"Mean",
"Median",
"Mode",
"Geo_mean",
"Geo_mean2",
"Geo_mean3",
"Har_mean",
"Trim_mean_5",
"Trim_mean_10",
"Trim_mean_20",
"IQ_mean",
"Tri_mean",
"Mn_AD_mn",
"Mn_AD_md",
"Md_AD_mn",
"Md_AD_md",
"MAD",
"Max_AD_mn",
"Max_AD_md",
"RMS",
"Min",
"Max",
"Quartiles",
"IQR",
"Low_notch",
"High_notch",
"Range",
"Deciles",
"Variance",
"SD",
"Skew",
"Kurtosis",
"Uniformity")
names(metrics) <- stat_names
if(use_type == "single") {
if(any(class(RIA_data_in) == "RIA_image"))
{
if(is.null(save_name)) {
txt <- automatic_name(RIA_data_in, use_orig, use_slot)
RIA_data_in$stat_glcm[[txt]] <- metrics
}
if(!is.null(save_name)) {RIA_data_in$stat_glcm[[save_name]] <- metrics
}
}
}
if(use_type == "glcm") {
if(any(class(RIA_data_in) == "RIA_image"))
{
if(is.null(save_name[i])) {
txt <- list_names[i]
RIA_data_in$stat_glcm[[txt]] <- metrics
}
if(!is.null(save_name[i])) {RIA_data_in$stat_glcm[[save_name[i]]] <- metrics
}
}
}
if(is.null(save_name)) {txt_name <- txt
} else {txt_name <- save_name}
if(verbose_in) {message(paste0("GLCM STATISTICS WAS SUCCESSFULLY ADDED TO '", txt_name, "' SLOT OF RIA_image$stat_glcm\n"))}
}
if(any(class(RIA_data_in) == "RIA_image") ) return(RIA_data_in)
else return(metrics)
}
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