Skip to main content

Table 1 Summary of feature groups in proposed Radiomics-Driven Statistical Textural Distinctiveness (RD-STD) [14]

From: MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection

Feature group Number of features Description
Textural (1st-order) 4 Mean, Standard deviation, Kurtosis, Skewness
   Energy, contrast, correlation, variance, inverse difference moment normalized,
Textural (2nd-order) 72 Sum average, sum variance, entropy, sum entropy, difference entropy,
  (18 in each of 4 directions) Information measure of correlation, homogeneity, autocorrelation
   Difference variance, dissimilarity, cluster shade, cluster prominence, maximum probability
Gabor filters 12 3 scales and 4 orientations
Kirsch filters 8 8 directions
Total 96 All features