pyradiomics官方文档学习(4)--Pyradiomic

pyradiomics 官方文档里有几个示例文件，里面涉及了包括yaml文件设置、feature extraction、可视化等一系列影像组学常规操作，是非常好的学习资料。源文件链接：https://github.com/AIM-Harvard/pyradiomics

今天学习标题名为"Example of using the PyRadiomics toolbox in Python"的这份文档。

0 导入模块(import modules)

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

1. 导入数据(Setting up data)

# 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))

2. 设置提取器(Instantiating the extractor)

Now that we have our input, we need to define the parameters and instantiate the extractor. For this there are three possibilities:
a. Use defaults, don't define custom settings
b. Define parameters in a dictionary, control filters and features after initialisation
c. Use a parameter file

Method 1, use defaults

# 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)

image.png

Method 2, hard-coded settings

# 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

image.png

# 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

image.png

# 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)

image.png

Method 3, using a parameter file

# 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)

image.png

3. 提取特征值(Extract features)

result = extractor.execute(imagePath, maskPath)

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)

image(part).png