[FEAT EXTRACTION] Why do GLDM "small dependence emphasis" and "low gray level emphasis" have the same formulas?

[nimayousefi75]

Hi

Recently I checked radiomics and saw these two formulas are the same but the output is not, why?
Am I missing something?

**1. Small Dependence Emphasis (SDE)**

.. math::
  SDE = \frac{\sum^{N_g}_{i=1}\sum^{N_d}_{j=1}{\frac{\textbf{P}(i,j)}{i^2}}}{N_z}

A measure of the distribution of small dependencies, with a greater value indicative
of smaller dependence and less homogeneous textures.
"""
pd = self.coefficients['pd']
jvector = self.coefficients['jvector']
Nz = self.coefficients['Nz']  # Nz = Np, see class docstring

sde = numpy.sum(pd / (jvector[None, :] ** 2), 1) / Nz
return sde`




**9. Low Gray Level Emphasis (LGLE)**

.. math::
  LGLE = \frac{\sum^{N_g}_{i=1}\sum^{N_d}_{j=1}{\frac{\textbf{P}(i,j)}{i^2}}}{N_z}

Measures the distribution of low gray-level values, with a higher value indicating a greater
concentration of low gray-level values in the image.
"""
pg = self.coefficients['pg']
ivector = self.coefficients['ivector']
Nz = self.coefficients['Nz']

lgle = numpy.sum(pg / (ivector[None, :] ** 2), 1) / Nz
return lgle

`