Merge pull request #740 from lyhyl/master

fix typos

docs/removedfeatures.rst

@@ -186,7 +186,7 @@ The mathematical proof is as follows:
 
 (4) Because in the PyRadiomics definition incomplete dependence zones are allowed, every voxel in the ROI has a
     dependence zone. Therefore, :math:`N_z = N_p` and :math:`\sum^{N_d}_{j=1}{\textbf{P}(i,j)}` equals the number of voxels
-    with gray level :math:`i` and is equal to :math:`\textbf{P}(i)`, the first order histogram with :math:`N_g` discreet
+    with gray level :math:`i` and is equal to :math:`\textbf{P}(i)`, the first order histogram with :math:`N_g` discrete
     gray levels, as defined in first order.
 
 (5) As defined in first order, :math:`p(i) = \frac{\textbf{P}(i)}{N_p}`

radiomics/glcm.py

@@ -47,8 +47,8 @@ class RadiomicsGLCM(base.RadiomicsFeaturesBase):
   - :math:`p(i,j)` be the normalized co-occurence matrix and equal to
     :math:`\frac{\textbf{P}(i,j)}{\sum{\textbf{P}(i,j)}}`
   - :math:`N_g` be the number of discrete intensity levels in the image
-  - :math:`p_x(i) = \sum^{N_g}_{j=1}{P(i,j)}` be the marginal row probabilities
-  - :math:`p_y(j) = \sum^{N_g}_{i=1}{P(i,j)}` be the marginal column probabilities
+  - :math:`p_x(i) = \sum^{N_g}_{j=1}{p(i,j)}` be the marginal row probabilities
+  - :math:`p_y(j) = \sum^{N_g}_{i=1}{p(i,j)}` be the marginal column probabilities
   - :math:`\mu_x` be the mean gray level intensity of :math:`p_x` and defined as
     :math:`\mu_x = \displaystyle\sum^{N_g}_{i=1}{p_x(i)i}`
   - :math:`\mu_y` be the mean gray level intensity of :math:`p_y` and defined as
@@ -354,7 +354,7 @@ def getCorrelationFeatureValue(self):
     linear dependency of gray level values to their respective voxels in the GLCM.
 
     .. note::
-      When there is only 1 discreet gray value in the ROI (flat region), :math:`\sigma_x` and :math:`\sigma_y` will be
+      When there is only 1 discrete gray value in the ROI (flat region), :math:`\sigma_x` and :math:`\sigma_y` will be
       0. In this case, an arbitrary value of 1 is returned instead. This is assessed on a per-angle basis.
     """
     eps = self.coefficients['eps']

radiomics/gldm.py

@@ -35,8 +35,8 @@ class RadiomicsGLDM(base.RadiomicsFeaturesBase):
 
   Let:
 
-  - :math:`N_g` be the number of discreet intensity values in the image
-  - :math:`N_d` be the number of discreet dependency sizes in the image
+  - :math:`N_g` be the number of discrete intensity values in the image
+  - :math:`N_d` be the number of discrete dependency sizes in the image
   - :math:`N_z` be the number of dependency zones in the image, which is equal to
     :math:`\sum^{N_g}_{i=1}\sum^{N_d}_{j=1}{\textbf{P}(i,j)}`
   - :math:`\textbf{P}(i,j)` be the dependence matrix

radiomics/glrlm.py

@@ -32,8 +32,8 @@ class RadiomicsGLRLM(base.RadiomicsFeaturesBase):
 
   Let:
 
-  - :math:`N_g` be the number of discreet intensity values in the image
-  - :math:`N_r` be the number of discreet run lengths in the image
+  - :math:`N_g` be the number of discrete intensity values in the image
+  - :math:`N_r` be the number of discrete run lengths in the image
   - :math:`N_p` be the number of voxels in the image
   - :math:`N_r(\theta)` be the number of runs in the image along angle :math:`\theta`, which is equal to
     :math:`\sum^{N_g}_{i=1}\sum^{N_r}_{j=1}{\textbf{P}(i,j|\theta)}` and :math:`1 \leq N_r(\theta) \leq N_p`

radiomics/glszm.py

@@ -35,8 +35,8 @@ class RadiomicsGLSZM(base.RadiomicsFeaturesBase):
 
   Let:
 
-  - :math:`N_g` be the number of discreet intensity values in the image
-  - :math:`N_s` be the number of discreet zone sizes in the image
+  - :math:`N_g` be the number of discrete intensity values in the image
+  - :math:`N_s` be the number of discrete zone sizes in the image
   - :math:`N_p` be the number of voxels in the image
   - :math:`N_z` be the number of zones in the ROI, which is equal to :math:`\sum^{N_g}_{i=1}\sum^{N_s}_{j=1}
     {\textbf{P}(i,j)}` and :math:`1 \leq N_z \leq N_p`

radiomics/ngtdm.py

@@ -68,7 +68,7 @@ class RadiomicsNGTDM(base.RadiomicsFeaturesBase):
   0 & \mbox{for} & n_i = 0 \end{array}}\right.`
   be the sum of absolute differences for gray level :math:`i`
 
-  :math:`N_g` be the number of discreet gray levels
+  :math:`N_g` be the number of discrete gray levels
 
   :math:`N_{g,p}` be the number of gray levels where :math:`p_i \neq 0`