old_contents: import os
  import SimpleITK as sitk
  from radiomics import featureextractor
  
  dataDir = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + ".." + os.path.sep + "data"
  #imageName = str(dataDir + os.path.sep + 'prostate_phantom_subvolume.nrrd')
  #maskName = str(dataDir + os.path.sep + 'prostate_phantom_subvolume-label.nrrd')
  imageName = str(dataDir + os.path.sep + 'brain1_image.nrrd')
  maskName = str(dataDir + os.path.sep + 'brain1_label.nrrd')
  
  if not os.path.exists(imageName):
    print 'Error: problem finding input image',imageName
    exit()
  if not os.path.exists(maskName):
    print 'Error: problem finding input labelmap',maskName
    exit()
  
  # Define settings for signature calculation
  # These are currently set equal to the respective default values
  kwargs = {}
  kwargs['binWidth'] = 25
  kwargs['resampledPixelSpacing'] = None  # [3,3,3] is an example for defining resampling (voxels with size 3x3x3mm)
  kwargs['interpolator'] = sitk.sitkBSpline
  kwargs['verbose'] = True
  
  # Initialize wrapperClass to generate signature
  extractor = featureextractor.RadiomicsFeaturesExtractor(**kwargs)
  
  # Only enable the original image as input (disables LoG, Wavelet)
  extractor.enableInputImages(original= {})
  
  # Disable all classes except firstorder
  extractor.disableAllFeatures()
  
  # Enable all features in firstorder
  # Sig.enableFeatureClassByName('firstorder')
  
  # Only enable mean and skewness in firstorder
  extractor.enableFeaturesByName(firstorder= ['Mean', 'Skewness'])
  
  for f in extractor.getFeaturesNames('firstorder'):
    print "Feature: %s" %(f)
    print eval('Sig.featureClasses["firstorder"].get'+f+'FeatureValue.__doc__')
  
  print "Calculating features"
  featureVector = extractor.execute(imageName, maskName)
  
  for featureName in featureVector.keys():
    print "Computed %s: %s" %(featureName, featureVector[featureName])
  
new_contents: import os
  import SimpleITK as sitk
  from radiomics import featureextractor
  
  dataDir = os.path.dirname(os.path.abspath(__file__)) + os.path.sep + ".." + os.path.sep + "data"
  #imageName = str(dataDir + os.path.sep + 'prostate_phantom_subvolume.nrrd')
  #maskName = str(dataDir + os.path.sep + 'prostate_phantom_subvolume-label.nrrd')
  imageName = str(dataDir + os.path.sep + 'brain1_image.nrrd')
  maskName = str(dataDir + os.path.sep + 'brain1_label.nrrd')
  
  if not os.path.exists(imageName):
    print 'Error: problem finding input image',imageName
    exit()
  if not os.path.exists(maskName):
    print 'Error: problem finding input labelmap',maskName
    exit()
  
  # Define settings for signature calculation
  # These are currently set equal to the respective default values
  kwargs = {}
  kwargs['binWidth'] = 25
  kwargs['resampledPixelSpacing'] = None  # [3,3,3] is an example for defining resampling (voxels with size 3x3x3mm)
  kwargs['interpolator'] = sitk.sitkBSpline
  kwargs['verbose'] = True
  
  # Initialize wrapperClass to generate signature
  extractor = featureextractor.RadiomicsFeaturesExtractor(**kwargs)
  
  # Only enable the original image as input (disables LoG, Wavelet)
  extractor.enableInputImages(original= {})
  
  # Disable all classes except firstorder
  extractor.disableAllFeatures()
  
  # Enable all features in firstorder
  # Sig.enableFeatureClassByName('firstorder')
  
  # Only enable mean and skewness in firstorder
  extractor.enableFeaturesByName(firstorder= ['Mean', 'Skewness'])
  
  for f in extractor.getFeaturesNames('firstorder'):
    print "Feature: %s" %(f)
    print eval('extractor.featureClasses["firstorder"].get'+f+'FeatureValue.__doc__')
  
  print "Calculating features"
  featureVector = extractor.execute(imageName, maskName)
  
  for featureName in featureVector.keys():
    print "Computed %s: %s" %(featureName, featureVector[featureName])
  
subject: Fix name bug in helloradiomics.py