Welcome to collageradiomics’s documentation!¶

CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.

CoLlAGe is based on the hypothesis that disruption in tissue microarchitecture can be quantified on imaging by measuring the disorder in voxel-wise gradient orientations. CoLlAGe involves assigning every image voxel a ‘disorder value’ associated with the co-occurrence matrix of gradient orientations computed around every voxel.

Details on extraction of CoLlAGe features are included in [1]. After feature extraction, the subsequent distribution or different statistics such as mean, median, variance etc can be computed and used in conjunction with a machine learning classifier to distinguish similar appearing pathologies. The feasibility of CoLlAGe in distinguishing cancer from treatment confounders/benign conditions and characterizing molecular subtypes of cancers has been demonstrated in the context of multiple challenging clinical problems.

Helpful Links¶

Instructions: README

RadxTools Website: https://radxtools.github.io

Original Paper: Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

Code Documentation¶

Notes¶

The attributes below represent the public output intended to be available to consumers of this module.

class collageradiomics.Collage(img_array, mask_array, svd_radius=5, verbose_logging=False, cooccurence_angles=[0.0, 0.7853981633974483, 1.5707963267948966, 2.356194490192345, 3.141592653589793, 3.9269908169872414, 4.71238898038469, 5.497787143782138], difference_variance_interpretation=DifferenceVarianceInterpretation.XMinusYVariance, haralick_window_size=-1, num_unique_angles=64)¶

Bases: object

This is the main object in the Collage calculation system. Usage: create a Collage object and then call the execute() function.

Parameters

image_array (numpy.ndarray) – image to run collage upon
mask_array (numpy.ndarray) – mask that correlates with the image
svd_radius (int, optional) – radius of svd. Defaults to 5.
verbose_logging (bool, optional) – This parameter is now ignored. Please use the python logging module.
cooccurence_angles (list, optional) – list of angles to use in the cooccurence matrix. Defaults to [x*numpy.pi/4 for x in range(8)]
difference_variance_interpretation (DifferenceVarianceInterpretation, optional) – Feature 10 has two interpretations, as the variance of |x-y| or as the variance of P(|x-y|).].Defaults to DifferenceVarianceInterpretation.XMinusYVariance.
haralick_window_size (int, optional) – size of rolling window for texture calculations. Defaults to -1.
num_unique_angles (int, optional) – number of bins to use for the texture calculation. Defaults to 64.

Attributes

collage_output: Array representing collage upon the mask within the full images.
cooccurence_angles: Iterable of angles that will be used in the cooccurence matrix.
difference_variance_interpretation: Feature 10 has two interpretations, as the variance of |x-y| or as the variance of P(|x-y|).].
haralick_window_size: Number of pixels around each pixel to calculate a haralick texture.
img_array: The original image.
is_3D: Whether we are using 3D collage calculations (True) or 2D (False)
mask_array: Array passed into Collage.
num_unique_angles: Number of bins to use for texture calculations.
svd_radius: SVD radius is used to calculate the pixel radius for the dominant angle calculation.
verbose_logging: This parameter is now ignored.

Methods

`execute`()	Begins haralick calculation.
`get_single_feature_output`(which_feature)	Output a single collage output feature.

property collage_output¶

Array representing collage upon the mask within the full images. If the input was 2D, the output will be height×width×13 where “13” is the number of haralick textures. If the input was 3D, the output will be height×width×depth×13x2 where “2” is the primary angle (element 0) or the secondary angle (element 1)

The output will have numpy.nan values everywhere outside the masked region.

Getter: Returns array the same shape as the original image with collage in the mask region.
Type: numpy.ndarray

property cooccurence_angles¶

Iterable of angles that will be used in the cooccurence matrix.

Getter: Returns the Iterable of cooccurence angles.
Setter: Sets the angles to be used in the cooccurence matrix.
Type: int

property difference_variance_interpretation¶

Feature 10 has two interpretations, as the variance of |x-y| or as the variance of P(|x-y|).]. Defaults to DifferenceVarianceInterpretation.XMinusYVariance.

Getter: Returns requested variance interpretation.
Setter: Sets requested variance interpretation.
Type: DifferenceVarianceInterpretation

execute()¶

Begins haralick calculation.

Returns: An image at original size that only has the masked section filled in with collage calculations.
Return type: numpy.ndarray

get_single_feature_output(which_feature)¶

Output a single collage output feature. If this was a 3D calculation, the output will be of size height×width×depth×2 where the “2” represents the collage calculation from the primary angle (0) or secondary angle (1).

param which_feature

Either an integer from 0 to 12 (inclusive) or a HaralickFeature enum value

:type which_feature HaralickFeature

property haralick_window_size¶

Number of pixels around each pixel to calculate a haralick texture.

Getter: Returns requested number of pixels.
Setter: Sets requested number of pixels.
Type: int

property img_array¶

The original image.

Getter: Returns the original image array.
Setter: Sets the original image array.
Type: np.ndarray

property is_3D¶: Whether we are using 3D collage calculations (True) or 2D (False)

property mask_array¶

Array passed into Collage.

Getter: Returns the original mask array.
Setter: Sets the original mask array.
Type: np.ndarray

property num_unique_angles¶

Number of bins to use for texture calculations. Defaults to 64.

Getter: Returns requested number of unique angles to bin into.
Type: int

property svd_radius¶

SVD radius is used to calculate the pixel radius for the dominant angle calculation.

Getter: Returns the SVD radius.
Setter: Sets the SVD radius.
Type: int

property verbose_logging¶

This parameter is now ignored. Please use the python logging module.

Getter: Returns True if on.
Setter: Turns verbose logging off or on.
Type: bool

class collageradiomics.DifferenceVarianceInterpretation(value)¶

Bases: Enum

Feature 10 has two interpretations, as the variance of |x-y| or as the variance of P(|x-y|). See: https://ieeexplore.ieee.org/document/4309314

Parameters: Enum (DifferenceVarianceInterpretation) – Enumeration Helper For Haralick Features

ProbabilityXMinusYVariance = 1¶

XMinusYVariance = 0¶

class collageradiomics.HaralickFeature(value)¶

Bases: IntEnum

Enumeration Helper For Haralick Features

Parameters: IntEnum (HaralickFeature) – Enumeration Helper For Haralick Features

AngularSecondMoment = 0¶

Contrast = 1¶

Correlation = 2¶

DifferenceEntropy = 9¶

DifferenceVariance = 8¶

Entropy = 7¶

InformationMeasureOfCorrelation1 = 10¶

InformationMeasureOfCorrelation2 = 11¶

MaximalCorrelationCoefficient = 12¶

SumAverage = 4¶

SumEntropy = 6¶

SumOfSquareVariance = 3¶

SumVariance = 5¶

Welcome to collageradiomics’s documentation!¶

Helpful Links¶

Code Documentation¶

Notes¶

Indices and tables¶