STYL: Rename signature for available input image types

Functions to yield original and derived images are now found by checking for the signature ("get...Image") in `imageoperations`, where ... is replaced by "Original" or by the filter name (e.g. Wavelet).
This more accurately reflects what these functions return (as "Original" isn't a filtered image) and is more similar to the signature used for dynamic enumeration of individual features in each feature class.

Author: JoostJM

radiomics/featureextractor.py

@@ -90,8 +90,11 @@ def __init__(self, *args, **kwargs):
 
   def addProvenance(self, provenance_on=True):
     """
-    Enable or disable reporting of settings used for calculated filters.
-    By default, settings used are added to the dictionary of calculated features as {"settings_<filter>":<settings>}
+    Enable or disable reporting of additional information on the extraction. This information includes toolbox version,
+    enabled input images and applied settings. Furthermore, additional information on the image and region of interest
+    (ROI) is also provided, including original image spacing, total number of voxels in the ROI and total number of
+    fully connected volumes in the ROI.
+
     To disable this, call ``addProvenance(False)``
     """
     self.provenance_on = provenance_on
@@ -184,16 +187,16 @@ def enableInputImageByName(self, inputImage, enabled=True, customArgs=None):
     Enable or disable specified input image. If enabling input image, optional custom settings can be specified in
     customArgs.
 
-    Current possible filters are:
+    Current possible input images are:
 
     - Original: No filter applied
     - Wavelet: Wavelet filtering, yields 8 decompositions per level (all possible combinations of applying either
       a High or a Low pass filter in each of the three dimensions.
-      See also :py:func:`~radiomics.imageoperations.applyFilterWavelet`
+      See also :py:func:`~radiomics.imageoperations.getWaveletImage`
     - LoG: Laplacian of Gaussian filter, edge enhancement filter. Emphasizes areas of gray level change, where sigma
       defines how coarse the emphasised texture should be. A low sigma emphasis on fine textures (change over a
       short distance), where a high sigma value emphasises coarse textures (gray level change over a large distance).
-      See also :py:func:`~radiomics.imageoperations.applyFilterLoG`
+      See also :py:func:`~radiomics.imageoperations.getLoGImage`
     - Square: Takes the square of the image intensities and linearly scales them back to the original range.
       Negative values in the original image will be made negative again after application of filter.
     - SquareRoot: Takes the square root of the absolute image intensities and scales them back to original range.
@@ -205,10 +208,10 @@ def enableInputImageByName(self, inputImage, enabled=True, customArgs=None):
 
     For the mathmetical formulas of square, squareroot, logarithm and exponential, see their respective functions in
     :ref:`imageoperations<radiomics-imageoperations-label>`
-    (:py:func:`~radiomics.imageoperations.applyFilterSquare`,
-    :py:func:`~radiomics.imageoperations.applyFilterSquareRoot`,
-    :py:func:`~radiomics.imageoperations.applyFilterLogarithm` and
-    :py:func:`~radiomics.imageoperations.applyFilterExponential`,
+    (:py:func:`~radiomics.imageoperations.getSquareImage`,
+    :py:func:`~radiomics.imageoperations.getSquareRootImage`,
+    :py:func:`~radiomics.imageoperations.getLogarithmImage` and
+    :py:func:`~radiomics.imageoperations.getExponentialImage`,
     respectively).
     """
     if enabled:
@@ -275,9 +278,9 @@ def execute(self, imageFilepath, maskFilepath, label=None):
     Compute radiomics signature for provide image and mask combination.
     First, image and mask are loaded and resampled if necessary. Second, if enabled, provenance information is
     calculated and stored as part of the result. Next, shape features are calculated on a cropped (no padding) version
-    of the original image. Then other featureclasses are calculated using all specified filters in inputImages. Images
-    are cropped to tumor mask (no padding) after application of filter and before being passed to the feature class.
-    Finally, the dictionary containing all calculated features is returned.
+    of the original image. Then other featureclasses are calculated using all specified input images in ``inputImages``.
+    Images are cropped to tumor mask (no padding) after application of any filter and before being passed to the feature
+    class. Finally, the dictionary containing all calculated features is returned.
 
     :param imageFilepath: SimpleITK Image, or string pointing to image file location
     :param maskFilepath: SimpleITK Image, or string pointing to labelmap file location
@@ -324,7 +327,7 @@ def execute(self, imageFilepath, maskFilepath, label=None):
       args = self.kwargs.copy()
       args.update(customKwargs)
       self.logger.info("Applying filter: '%s' with settings: %s" % (imageType, str(args)))
-      imageGenerators = chain(imageGenerators, eval('imageoperations.applyFilter%s(image, **args)' % (imageType)))
+      imageGenerators = chain(imageGenerators, eval('imageoperations.get%sImage(image, **args)' % (imageType)))
 
     # Calculate features for all (filtered) images in the generator
     for inputImage, inputImageName, inputKwargs in imageGenerators:
@@ -421,11 +424,11 @@ def computeFeatures(self, image, mask, inputImageName, **kwargs):
   def getInputImageTypes(cls):
     """
     Returns a list of possible input image types. This function finds the image types dynamically by matching the
-    signature ("applyFilter<filterName>") against functions defined in :ref:`imageoperations
-    <radiomics-imageoperations-label>`. Returns a list containing available filter names (<filterName> part of the
+    signature ("get<inputImage>Image") against functions defined in :ref:`imageoperations
+    <radiomics-imageoperations-label>`. Returns a list containing available input image names (<inputImage> part of the
     corresponding function name).
     """
-    return [member[11:] for member in dir(imageoperations) if member.startswith('applyFilter')]
+    return [member[3:-5] for member in dir(imageoperations) if member.startswith('get') and member.endswith("Image")]
 
   @classmethod
   def getFeatureClasses(cls):

radiomics/imageoperations.py

@@ -239,7 +239,7 @@ def applyThreshold(inputImage, lowerThreshold, upperThreshold, insideValue=None,
   return tif.Execute(inputImage)
 
 
-def applyFilterOriginal(inputImage, **kwargs):
+def getOriginalImage(inputImage, **kwargs):
   """
   This function does not apply any filter, but returns the original image. This function is needed to
   dyanmically expose the original image as a valid input image.
@@ -249,7 +249,7 @@ def applyFilterOriginal(inputImage, **kwargs):
   yield inputImage, "original", kwargs
 
 
-def applyFilterLoG(inputImage, **kwargs):
+def getLoGImage(inputImage, **kwargs):
   """
   Apply Laplacian of Gaussian filter to input image and compute signature for each filtered image.
 
@@ -299,7 +299,7 @@ def applyFilterLoG(inputImage, **kwargs):
       logger.warning('applyLoG: sigma must be greater than 0.0: %g', sigma)
 
 
-def applyFilterWavelet(inputImage, **kwargs):
+def getWaveletImage(inputImage, **kwargs):
   """
       Apply wavelet filter to image and compute signature for each filtered image.
 
@@ -446,7 +446,7 @@ def _decompose_k(data, wavelet):
   return H, L
 
 
-def applyFilterSquare(inputImage, **kwargs):
+def getSquareImage(inputImage, **kwargs):
   r"""
   Computes the square of the image intensities.
 
@@ -468,7 +468,7 @@ def applyFilterSquare(inputImage, **kwargs):
   yield im, "square", kwargs
 
 
-def applyFilterSquareRoot(inputImage, **kwargs):
+def getSquareRootImage(inputImage, **kwargs):
   r"""
   Computes the square root of the absolute value of image intensities.
 
@@ -493,7 +493,7 @@ def applyFilterSquareRoot(inputImage, **kwargs):
   yield im, "squareroot", kwargs
 
 
-def applyFilterLogarithm(inputImage, **kwargs):
+def getLogarithmImage(inputImage, **kwargs):
   r"""
   Computes the logarithm of the absolute value of the original image + 1.
 
@@ -519,7 +519,7 @@ def applyFilterLogarithm(inputImage, **kwargs):
   yield im, "logarithm", kwargs
 
 
-def applyFilterExponential(inputImage, **kwargs):
+def getExponentialImage(inputImage, **kwargs):
   r"""
   Computes the exponential of the original image.