Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics
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
Hi
Recently I checked radiomics and saw these two formulas are the same but the output is not, why? Am I missing something?
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