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Table 1 Summary of textural features used in the feature model

From: Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models

Feature class


First-order statistical features

Mean, Standard deviation





Second-order statistical

Energy, Contrast

features (Haralick)

Correlation, Variance


Inverse difference moment


Sum average, Sum variance


Sum entropy, Entropy


Difference variance


Difference entropy


Information measure of correlation


Homogeneity, Autocorrelation


Dissimilarity, Cluster shade


Cluster prominence


Maximum probability

Gabor filters

3 scales and 4 orientations

Kirsch filters

8 directions