# Customizing the Extraction¶

## Types of Customization¶

There are 3 ways in which the feature extraction can be customized in PyRadiomics: 1) Specifying which image types (original/derived) to use to extract features from, 2) Specifying which feature(class) to extract, 3) Specifying settings, which control the pre processing and customize the behaviour of filters and feature classes.

### Image Types¶

These are the input image types (either the original image or derived images) that can be used to extract features from. The image types that are available are determined dynamically (all functions in imageoperations.py that fit the signature of a filter.

The enabled types are stored in the inputImages dictionary in the feature extractor class instance and can be changed using the functions enableAllInputImages(), disableAllInputImages(), enableInputImageByName() and enableInputImages(). Moreover, custom settings can be provided for each enabled input type, which will then only be applied for that input image type. Please note that this will only work for settings that are applied at or after any filter is applied (i.e. not at the feature extractor level).

By default, only the original input type is enabled.

### Enabled Features¶

These are the features that are extracted from each (original and/or derived) input image. The available features are determined dynamically, and are ordered in feature classes. For more information on the signature used to identify features and feature classes, see the Developers <radiomics-developers> section.

The enable features are stored in the enabledFeatures dictionary in the feature extractor class instance and can be changed using the functions enableAllFeatures(), disableAllFeatures(), enableFeatureClassByName() and enableFeaturesByName(). Each key-value pair in the dictionary represents one enabled feature class with the feature class name as the key and a list of enabled feature names as value. If the value is None or an empty list, all features in that class are enabled. Otherwise only the features specified.

By default, all feature classes and all features are enabled.

### Settings¶

Besides customizing what to extract (image types, features), PyRadiomics exposes various settings customizing how the features are extracted. These settings operate at different levels. E.g. resampling is done just after the images are loaded (in the feature extractor), so settings controlling the resampling operate only on the feature extractor level. Settings are stored in the setttings dictionary in the feature extractor class instance, where the key is the case sensitive setting name. Custom settings are provided as keyword arguments at initialization of the feature extractor (with the setting name as keyword and value as the argument value, e.g. binWidth=25), or by interacting directly with the settings dictionary.

Note

When using the feature classes directly, feature class level settings can be customized by providing them as keyword arguments at initialization of the feature class.

Below are the settings that control the behaviour of the extraction, ordered per level and category. Each setting is listed as it’s unique, case sensitive name, followed by it’s default value in brackets. After the default value is the documentation on the type of the value and what the setting controls.

#### Feature Extractor Level¶

Image Normalization

• normalize [False]: Boolean, set to True to enable normalizing of the image before any resampling. See also normalizeImage().
• normalizeScale [1]: Float, > 0, determines the scale after normalizing the image. If normalizing is disabled, this has no effect.
• removeOutliers [None]: Float, > 0, defines the outliers to remove from the image. An outlier is defined as values that differ more than $$n\sigma_x$$ from the mean, where $$n>0$$ and equal to the value of this setting. If this parameter is omitted (providing it without a value (i.e. None) in the parameter file will throw an error), no outliers are removed. If normalizing is disabled, this has no effect. See also normalizeImage().

Resampling the image

• resampledPixelSpacing [None]: List of 3 floats (> 0), sets the size of the voxel in (x, y, z) plane when resampling.

• interpolator [sitkBSpline]: Simple ITK constant or string name thereof, sets interpolator to use for resampling. Enumerated value, possible values:

• sitkNearestNeighbor (= 1)
• sitkLinear (= 2)
• sitkBSpline (= 3)
• sitkGaussian (= 4)
• sitkLabelGaussian (= 5)
• sitkHammingWindowedSinc (= 6)
• sitkCosineWindowedSinc (= 7)
• sitkWelchWindowedSinc (= 8)
• sitkLanczosWindowedSinc (= 9)
• sitkBlackmanWindowedSinc (= 10)
• padDistance [5]: Integer, $$\geq 0$$, set the number of voxels pad cropped tumor volume with during resampling. Padding occurs in new feature space and is done on all faces, i.e. size increases in x, y and z direction by 2*padDistance. Padding is needed for some filters (e.g. LoG). Value of padded voxels are set to original gray level intensity, padding does not exceed original image boundaries. N.B. After application of filters image is cropped again without padding.

Note

Resampling is disabled when either resampledPixelSpacing or interpolator is set to None

Mask validation

• minimumROIDimensions [1]: Integer, range 1-3, specifies the minimum dimensions (1D, 2D or 3D, respectively). Single-voxel segmentations are always excluded.
• minimumROISize [None]: Integer, > 0, specifies the minimum number of voxels required. Test is skipped if this parameter is omitted (specifying it as None in the parameter file will throw an error).
• geometryTolerance [None]: Float, determines the tolarance used by SimpleITK to compare origin, direction and spacing between image and mask. Affects the fist step in checkMask(). If set to None, PyRadiomics will use SimpleITK default (1e-16).
• correctMask [False]: Boolean, if set to true, PyRadiomics will attempt to resample the mask to the image geometry when the first step in checkMask() fails. This uses a nearest neighbor interpolator. Mask check will still fail if the ROI defined in the mask includes areas outside of the image physical space.

Miscellaneous

#### Filter Level¶

Laplacian of Gaussian settings

• sigma: List of floats or integers, must be greater than 0. Sigma values to use for the filter (determines coarseness).

Warning

Setting for sigma must be provided if LoG filter is enabled. If omitted, no LoG image features are calculated and the function will return an empty dictionary.

Wavelet settings

• start_level [0]: integer, 0 based level of wavelet which should be used as first set of decompositions from which a signature is calculated

• level [1]: integer, number of levels of wavelet decompositions from which a signature is calculated.

• wavelet [“coif1”]: string, type of wavelet decomposition. Enumerated value, validated against possible values present in the pyWavelet.wavelist(). Current possible values (pywavelet version 0.4.0) (where an aditional number is needed, range of values is indicated in []):

• haar
• dmey
• sym[2-20]
• db[1-20]
• coif[1-5]
• bior[1.1, 1.3, 1.5, 2.2, 2.4, 2.6, 2.8, 3.1, 3.3, 3.5, 3.7, 3.9, 4.4, 5.5, 6.8]
• rbio[1.1, 1.3, 1.5, 2.2, 2.4, 2.6, 2.8, 3.1, 3.3, 3.5, 3.7, 3.9, 4.4, 5.5, 6.8]

#### Feature Class Level¶

Image discretization

• binWidth [25]: Float, > 0, size of the bins when making a histogram and for discretization of the image gray level.

Forced 2D extraction

• force2D [False]: Boolean, set to true to force a by slice texture calculation. Dimension that identifies the ‘slice’ can be defined in force2Ddimension. If input ROI is already a 2D ROI, features are automatically extracted in 2D. See also generateAngles()
• force2Ddimension [0]: int, range 0-2. Specifies the ‘slice’ dimension for a by-slice feature extraction. Value 0 identifies the ‘z’ dimension (axial plane feature extraction), and features will be extracted from the xy plane. Similarly, 1 identifies the y dimension (coronal plane) and 2 the x dimension (saggital plane). if force2Dextraction is set to False, this parameter has no effect. See also generateAngles()

Texture matrix weighting

• weightingNorm [None]: string, indicates which norm should be used when applying distance weighting. Enumerated setting, possible values:

• ‘manhattan’: first order norm
• ‘euclidean’: second order norm
• ‘infinity’: infinity norm.
• ‘no_weighting’: GLCMs are weighted by factor 1 and summed
• None: Applies no weighting, mean of values calculated on separate matrices is returned.

In case of other values, an warning is logged and option ‘no_weighting’ is used.

Note

This only affects the GLCM and GLRLM feature classes. Moreover, weighting is applied differently in those classes. For more information on how weighting is applied, see the documentation on GLCM and GLRLM.

#### Feature Class Specific Settings¶

First Order

• voxelArrayShift [0]: Integer, This amount is added to the gray level intensity in features Energy, Total Energy and RMS, this is to prevent negative values. If using CT data, or data normalized with mean 0, consider setting this parameter to a fixed value (e.g. 2000) that ensures non-negative numbers in the image. Bear in mind however, that the larger the value, the larger the volume confounding effect will be.

GLCM

• distances [[1]]: List of integers. This specifies the distances between the center voxel and the neighbor, for which angles should be generated. See also generateAngles()

## Parameter File¶

All 3 types of customization can be provided in a single yaml-structured text file, which can be provided in an optional argument (--param) when running pyradiomics from the command line. In interactive mode, it can be provided during initialization of the feature extractor, or using loadParams() after initialization. This removes the need to hard code a customized extraction in a python script through use of functions described above. Additionally, this also makes it more easy to share settings for customized extractions.

Note

Examples of the parameter file are provided in the pyradiomics/examples/exampleSettings folder.

The paramsFile is written according to the YAML-convention (www.yaml.org) and is checked by the code for consistency. Only one yaml document per file is allowed. Settings must be grouped by customization type as mentioned above. This is reflected in the structure of the document as follows:

<Customization Type>:
<Setting Name>: <value>
...
<Customization Type>:
...


Blank lines may be inserted to increase readability, these are ignored by the parser. Additional comments are also possible, these are preceded by an ‘#’ and can be inserted on a blank line, or on a line containing settings:

# This is a line containing only comments
setting: # This is a comment placed after the declaration of the 'setting' group.


Any keyword, such as a customization type or setting name may only be mentioned once. Multiple instances do not raise an error, but only the last one encountered is used.

The three setting types are named as follows:

1. inputImage: input image to calculate features on. <value> is custom kwarg settings (dictionary). if <value> is an empty dictionary (‘{}’), no custom settings are added for this input image.
2. featureClass: Feature class to enable, <value> is list of strings representing enabled features. If no <value> is specified or <value> is an empty list (‘[]’), all features for this class are enabled.
3. setting: Setting to use for pre processing and class specific settings. if no <value> is specified, the value for this setting is set to None.

Note

• settings not specified in parameters are set to their default value.
• enabledFeatures are replaced by those in parameters (i.e. only specified features/classes are enabled. If the ‘featureClass’ customization type is omitted, all featureClasses and features are enabled.
• inputImages are replaced by those in parameters (i.e. only specified types are used to extract features from. If the ‘inputImage’ customization type is ommited, only original image is used for feature extraction, with no additional custom settings.