glcm_stat_all.RdCalculates aggregated statistics of GLCM matrix statistics calculated on GCLM matrices evaluated in all different directions.
glcm_stat_all(RIA_data_in, statistic = "mean(X, na.rm = TRUE)",
verbose_in = TRUE)| RIA_data_in | RIA_image, created by |
|---|---|
| statistic | string, defining the statistic to be calculated on the array of GLCM statistics. By default, statistic is set to "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: median(X, na.rm = TRUE). |
| verbose_in | logical, indicating whether to print detailed information.
Most prints can also be suppressed using the |
RIA_image containing the statistical information.
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 http://circimaging.ahajournals.org/content/10/12/e006843
Márton KOLOSSVÁRY et al. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. Journal of Thoracic Imaging (2017). DOI: 10.1097/RTI.0000000000000268 https://www.ncbi.nlm.nih.gov/pubmed/28346329
if (FALSE) {
#Discretize loaded image and then calculate GLCM statistics for all matrices
RIA_image <- discretize(RIA_image, bins_in = c(4, 8), equal_prob = TRUE,
use_orig = TRUE, write_orig = FALSE)
RIA_image <- glcm_all(RIA_image, use_type = "discretized", distance = c(1:2))
RIA_image <- glcm_stat(RIA_image)
#Calculate the average of the different GLCM matrices in the different directions
RIA_image <- glcm_stat_all(RIA_image)
}