First, import some built-in Python modules needed to get our testing data.
Second, import the toolbox, only the featureextractor is needed, this module handles the interaction with other parts of the toolbox.
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from __future__ import print_function
import six
import os # needed navigate the system to get the input data
import radiomics
from radiomics import featureextractor # This module is used for interaction with pyradiomics
Test cases can be downloaded to temporary files. This is handled by the radiomics.getTestCase() function, which checks if the requested test case is available and if not, downloads it. It returns a tuple with the location of the image and mask of the requested test case, or (None, None) if it fails.
Alternatively, if the data is available somewhere locally, this directory can be passed as a second argument to radiomics.getTestCase(). If that directory does not exist or does not contain the testcase, functionality reverts to default and tries to download the test data.
If getting the test case fails, PyRadiomics will log an error explaining the cause.
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# Get the testCase
imagePath, maskPath = radiomics.getTestCase('brain1')
if imagePath is None or maskPath is None: # Something went wrong, in this case PyRadiomics will also log an error
raise Exception('Error getting testcase!') # Raise exception to prevent cells below from running in case of "run all"
# Additonally, store the location of the example parameter file, stored in \pyradiomics\examples/exampleSettings
paramPath = os.path.join('..', 'examples', 'exampleSettings', 'Params.yaml')
print('Parameter file, absolute path:', os.path.abspath(paramPath))
Now that we have our input, we need to define the parameters and instantiate the extractor. For this there are three possibilities:
Use defaults, don't define custom settings
Define parameters in a dictionary, control filters and features after initialisation
Use a parameter file
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# Instantiate the extractor
extractor = featureextractor.RadiomicsFeatureExtractor()
print('Extraction parameters:\n\t', extractor.settings)
print('Enabled filters:\n\t', extractor.enabledImagetypes)
print('Enabled features:\n\t', extractor.enabledFeatures)
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# First define the settings
settings = {}
settings['binWidth'] = 20
settings['sigma'] = [1, 2, 3]
# Instantiate the extractor
extractor = featureextractor.RadiomicsFeatureExtractor(**settings) # ** 'unpacks' the dictionary in the function call
print('Extraction parameters:\n\t', extractor.settings)
print('Enabled filters:\n\t', extractor.enabledImagetypes) # Still the default parameters
print('Enabled features:\n\t', extractor.enabledFeatures) # Still the default parameters
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# This cell is equivalent to the previous cell
extractor = featureextractor.RadiomicsFeatureExtractor(binWidth=20, sigma=[1, 2, 3]) # Equivalent of code above
print('Extraction parameters:\n\t', extractor.settings)
print('Enabled filters:\n\t', extractor.enabledImagetypes) # Still the default parameters
print('Enabled features:\n\t', extractor.enabledFeatures) # Still the default parameters
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# Enable a filter (in addition to the 'Original' filter already enabled)
extractor.enableImageTypeByName('LoG')
print('')
print('Enabled filters:\n\t', extractor.enabledImagetypes)
# Disable all feature classes, save firstorder
extractor.disableAllFeatures()
extractor.enableFeatureClassByName('firstorder')
print('')
print('Enabled features:\n\t', extractor.enabledFeatures)
# Specify some additional features in the GLCM feature class
extractor.enableFeaturesByName(glcm=['Autocorrelation', 'Homogeneity1', 'SumSquares'])
print('')
print('Enabled features:\n\t', extractor.enabledFeatures)
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# Instantiate the extractor
extractor = featureextractor.RadiomicsFeatureExtractor(paramPath)
print('Extraction parameters:\n\t', extractor.settings)
print('Enabled filters:\n\t', extractor.enabledImagetypes)
print('Enabled features:\n\t', extractor.enabledFeatures)
Now that we have our extractor set up with the correct parameters, we can start extracting features:
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result = extractor.execute(imagePath, maskPath)
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print('Result type:', type(result)) # result is returned in a Python ordered dictionary)
print('')
print('Calculated features')
for key, value in six.iteritems(result):
print('\t', key, ':', value)