Table 1 Parent child relationship on extracted GLCM features to distinguish the brain tumor types (pituitary and meningioma) using mutual information (MI), Kullback–Leibler (KL) divergence and Pearson’s correlation.

From: Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Parent

Child

KL divergence

Relative weight

Overall contribution (%)

Mutual information

Pearson’s correlation

p-value

Correlation

Correlation2

1.4640

1.0000

16.67

1.4640

1.0000

0.0000

Dissimilarity

Homogenity1

1.2708

0.8680

14.47

1.2708

− 0.9664

0.0000

Entropy

Energy

1.0340

0.7063

11.78

1.0408

− 0.8825

0.0000

Dissimilarity

Contrast

1.0238

0.6993

11.66

1.0238

0.9277

0.0000

Cluster shade

Cluster Prominance

0.9254

0.6321

10.54

0.9254

0.8888

0.0000

Homogenity1

Entropy

0.8624

0.5890

9.82

0.8624

− 0.78350

0.0000

Entropy

AutoCorrelation

0.7657

0.5230

8.72

0.4494

0.6651

0.0000

Correlation

Cluster Shade

0.4325

0.2954

4.92

0.4325

0.5575

0.0000

Correlation

Dissimilarity

0.4306

0.2941

4.90

0.4306

− 0.7149

0.0000

Cluster prominance

AutoCorrelation

0.3465

0.2366

3.94

0.0302

0.1798

0.0000

Cluster shade

Energy

0.2236

0.1527

2.54

0.2304

0.5184

0.0000

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