def getUniformityFeatureValue(self):
r"""
**19. Uniformity**
.. math::
\textit{uniformity} = \displaystyle\sum^{N_g}_{i=1}{p(i)^2}
Uniformity is a measure of the sum of the squares of each intensity value. This is a measure of the heterogeneity of
the image array, where a greater uniformity implies a greater heterogeneity or a greater range of discrete intensity
values.
.. note::
Defined by IBSI as Intensity Histogram Uniformity.
"""
eps = numpy.spacing(1)
binEdges = imageoperations.getBinEdges(self.binWidth,
self.targetVoxelArray)
bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
sumBins = bins.sum()
if sumBins == 0: # No segmented voxels
return 0
bins = bins / (float(sumBins + eps))
return numpy.sum(bins**2)
def getEntropyFeatureValue(self):
r"""
**3. Entropy**
.. math::
\textit{entropy} = -\displaystyle\sum^{N_g}_{i=1}{p(i)\log_2\big(p(i)+\epsilon\big)}
Here, :math:`\epsilon` is an arbitrarily small positive number (:math:`\approx 2.2\times10^{-16}`).
Entropy specifies the uncertainty/randomness in the image values. It measures the average amount of information
required to encode the image values.
.. note::
Defined by IBSI as Intensity Histogram Entropy.
"""
eps = numpy.spacing(1)
binEdges = imageoperations.getBinEdges(self.binWidth,
self.targetVoxelArray)
bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
sumBins = bins.sum()
if sumBins == 0: # No segmented voxels
return 0
bins = bins + eps
bins = bins / float(sumBins)
return -1.0 * numpy.sum(bins * numpy.log2(bins))
def _getDiscretizedTargetVoxelArray(self):
if self.discretizedTargetVoxelArray is None:
binEdges = imageoperations.getBinEdges(self.targetVoxelArray, **self.settings)
self.discretizedTargetVoxelArray = numpy.histogram(self.targetVoxelArray, binEdges)[0]
return self.discretizedTargetVoxelArray
def _getDiscretizedTargetVoxelArray(self):
if self.discretizedTargetVoxelArray is None:
if self.binCount is not None:
binEdges = self.binCount
else:
binEdges = imageoperations.getBinEdges(self.binWidth,
self.targetVoxelArray)
self.discretizedTargetVoxelArray = numpy.histogram(
self.targetVoxelArray, binEdges)[0]
return self.discretizedTargetVoxelArray
def getUniformityFeatureValue(self):
r"""
Calculate the Uniformity of the image array.
:math:`uniformity = \displaystyle\sum^{N_l}_{i=1}{p(i)^2}`
Uniformity is a measure of the sum of the squares of each intensity value. This is a measure of the heterogeneity of
the image array, where a greater uniformity implies a greater heterogeneity or a greater range of discrete intensity
values.
"""
eps = numpy.spacing(1)
binEdges = imageoperations.getBinEdges(self.binWidth, self.targetVoxelArray)
bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
try:
bins = bins / (float(bins.sum() + eps))
return (numpy.sum(bins ** 2))
except ZeroDivisionError:
return numpy.core.nan
def getEntropyFeatureValue(self):
r"""
Calculate the Entropy of the image array.
:math:`entropy = -\displaystyle\sum^{N_l}_{i=1}{p(i)\log_2\big(p(i)+\epsilon\big)}`
Here, :math:`\epsilon` is an arbitrarily small positive number (:math:`\approx 2.2\times10^{-16}`).
Entropy specifies the uncertainty/randomness in the image values. It measures the average amount of information
required to encode the image values.
"""
eps = numpy.spacing(1)
binEdges = imageoperations.getBinEdges(self.binWidth, self.targetVoxelArray)
bins = numpy.histogram(self.targetVoxelArray, binEdges)[0]
try:
bins = bins + eps
bins = bins / float(bins.sum())
return (-1.0 * numpy.sum(bins * numpy.log2(bins)))
except ZeroDivisionError:
return numpy.core.nan
