Table 1.

Features used for detection of the TIB patterns. Assume κ₁ and κ₂ indicate eigenvalues of the local Hessian matrix (H_e) for any given local patch ℒ, K and H indicate Gaussian and mean curvature respectively, dA is the induced area metrics on Σ, and ds is length metric on ∂Σ.

Extracted features	Definition
Willmore Energy	∫Σ |H|2 dA − ∫∂Σ |K|ds
Shape index	$\mathit{\text{SI}}=\frac{2}{\pi }\mathit{\text{arctan}}(\frac{{\kappa }_1+{\kappa }_2}{{\kappa }_1-{\kappa }_2})\in [-1,1]$
Gaussian curvature	K = κ1κ2
Mean curvature	H = (κ1 + κ2) / 2
Elongation	κ2 / κ1 with κ2 ≤ κ1
Distortion	|κ1 − κ2|
Shear	(κ1 − κ2)2 / 4
Compactness	$1/(4\pi \sqrt{{\kappa }_1{\kappa }_2})$
Grey level	autocorrelation, contrast, entropy,
Co-Occurrence	variance, dissimilarity, homogeneity,
Matrix (GLCM) based	cluster shade, energy, max probability,
texture features	sum of averages, difference of variance,
	sum of squares of variance, mutual information,
	sum of variance,sum of entropy
	difference of entropy, normalized inverse,
	cluster prominence, difference moment.