textureFeatures

Radiomics texture features

Since R2023b

Syntax

T = textureFeatures(R)

T = textureFeatures(R,Name=Value)

Description

T = textureFeatures(R) computes the radiomics texture features T for the radiomics object R.

T = textureFeatures(R,Name=Value) specifies additional options using one or more optional name-value arguments.

Examples

Compute Texture Features for Multiple ROIs in 2-D X-Ray Image

Load an X-ray image into the workspace as a medicalImage object. Visualize the image.

data = medicalImage("forearmXrayImage1.dcm");
I = data.Pixels;
figure
imshow(I,[])

Draw two regions of interest (ROI) in the X-ray image. Create masks from the ROIs.

roi1 = drawassisted(Color="g");
roi2 = drawassisted(Color="r");

mask1 = createMask(roi1,I);
mask2 = createMask(roi2,I);

Create an ROI label matrix, using different labels for the two ROIs. Create a medicalImage object of the ROI label data.

mask = zeros(size(I));
mask(mask1) = 1;
mask(mask2) = 2;
info = dicominfo("forearmXrayImage1.dcm");
roi = medicalImage(mask,info);

Create a radiomics object from the X-ray image data and ROI label data.

R = radiomics(data,roi)

R = 
  radiomics with properties:

                             Data: [1×1 medicalImage]
                         ROILabel: [1×1 medicalImage]
                         Resample: 0
                        Resegment: 1
                       Discretize: 1
                    DiscretizeIVH: 1
            ResampledVoxelSpacing: []
               DataResampleMethod: []
               MaskResampleMethod: []
              ResegmentationRange: []
                  ExcludeOutliers: 1
       DiscreteBinSizeOrBinNumber: []
                   DiscreteMethod: 'FixedBinNumber'
    DiscreteIVHBinSizeOrBinNumber: []
                DiscreteIVHMethod: 'FixedBinNumber'

Compute texture features for both ROIs.

T = textureFeatures(R)

T=2×137 table
    "1"    0.0025    57.1732      675.8153    11.0685    8.6729    46.5654    4.5233    114.3463    2.5804e+03    7.4309    5.9926e-04    122.8478    8.6729    0.1971    0.9492    0.1125    0.9951    0.1163    0.9090    3.8832e+03    2.5804e+03     1.3078e+05    23618430    -0.2975    0.9893    0.0016    57.1739      675.8519    11.5961
    "2"    0.0021    95.4544    1.0353e+03    11.4294    7.9161    41.0320    4.4079    190.9088    4.0368e+03    7.7995    4.6008e-04    104.5714    7.9161    0.2109    0.9534    0.1239    0.9958    0.1264    0.9495    1.0095e+04    4.0368e+03    -1.9066e+05    48382976    -0.3299    0.9944    0.0016    95.4542    1.0354e+03    11.8986

Compute Texture Features for 3-D Computed Tomography (CT) Image

Import a computed tomography (CT) image volume and the corresponding ROI mask volume from the IBSI validation data set [1][2][3] as medicalVolume objects.

unzip("CTImageMaskNIfTI.zip")
data = medicalVolume("CT_image.nii.gz");
roi = medicalVolume("CT_mask.nii.gz");

Visualize a slice of the CT image volume and the corresponding ROI.

figure
imshowpair(data.Voxels(:,:,20),roi.Voxels(:,:,20),"montage")

Create a radiomics object, using the CT image volume and ROI mask volume, with default preprocessing options.

R = radiomics(data,roi)

R = 
  radiomics with properties:

                             Data: [1×1 medicalVolume]
                         ROILabel: [1×1 medicalVolume]
                         Resample: 1
                        Resegment: 1
                       Discretize: 1
                    DiscretizeIVH: 1
            ResampledVoxelSpacing: 1
               DataResampleMethod: 'linear'
               MaskResampleMethod: 'linear'
              ResegmentationRange: []
                  ExcludeOutliers: 1
       DiscreteBinSizeOrBinNumber: []
                   DiscreteMethod: 'FixedBinNumber'
    DiscreteIVHBinSizeOrBinNumber: []
                DiscreteIVHMethod: 'FixedBinNumber'

Compute the grey level co-occurrence matrix (GLCM) texture features of the ROI in the 2-D resampled CT image volume.

I = textureFeatures(R,Type=["GLCM","GLRLM"],SubType="2D")

I=1×83 table
    LabelID    JointMaximumAveraged2D    JointAverageAveraged2D    JointVarianceAveraged2D    JointEntropyAveraged2D    DifferenceAverageAveraged2D    DifferenceVarianceAveraged2D    DifferenceEntropyAveraged2D    SumAverageAveraged2D    SumVarianceAveraged2D    SumEntropyAveraged2D    AngularSecondMomentAveraged2D    ContrastAveraged2D    DissimilarityAveraged2D    InverseDifferenceAveraged2D    NormalisedInverseDifferenceAveraged2D    InverseDifferenceMomentAveraged2D    NormalisedInverseDifferenceMomentAveraged2D    InverseVarianceAveraged2D    CorrelationAveraged2D    AutoCorrelationAveraged2D    ClusterTendencyAveraged2D    ClusterShadeAveraged2D    ClusterProminenceAveraged2D    InformationCorrelation1Averaged2D    InformationCorrelation2Averaged2D    JointMaximumSliceMerged2D    JointAverageSliceMerged2D    JointVarianceSliceMerged2D    JointEntropySliceMerged2D    DifferenceAverageSliceMerged2D    DifferenceVarianceSliceMerged2D    DifferenceEntropySliceMerged2D    SumAverageSliceMerged2D    SumVarianceSliceMerged2D    SumEntropySliceMerged2D    AngularSecondMomentSliceMerged2D    ContrastSliceMerged2D    DissimilaritySliceMerged2D    InverseDifferenceSliceMerged2D    NormalisedInverseDifferenceSliceMerged2D    InverseDifferenceMomentSliceMerged2D    NormalisedInverseDifferenceMomentSliceMerged2D    InverseVarianceSliceMerged2D    CorrelationSliceMerged2D    AutoCorrelationSliceMerged2D    ClusterTendencySliceMerged2D    ClusterShadeSliceMerged2D    ClusterProminenceSliceMerged2D    InformationCorrelation1SliceMerged2D    InformationCorrelation2SliceMerged2D    ShortRunsEmphasisAveraged2D    LongRunsEmphasisAveraged2D    LowGrayLevelRunEmphasisAveraged2D    HighGrayLevelRunEmphasisAveraged2D    ShortRunLowGrayLevelEmphasisAveraged2D    ShortRunHighGrayLevelEmphasisAveraged2D    LongRunLowGrayLevelEmphasisAveraged2D    LongRunHighGrayLevelEmphasisAveraged2D    GrayLevelNonUniformityAveraged2D    NormalisedGrayLevelNonUniformityAveraged2D    RunLengthNonUniformityAveraged2D    NormalisedRunLengthNonUniformityAveraged2D    RunPercentageAveraged2D    GrayLevelVarianceAveraged2D    RunLengthVarianceAveraged2D    RunEntropyAveraged2D    ShortRunsEmphasisSliceMerged2D    LongRunsEmphasisSliceMerged2D    LowGrayLevelRunEmphasisSliceMerged2D    HighGrayLevelRunEmphasisSliceMerged2D    ShortRunLowGrayLevelEmphasisSliceMerged2D    ShortRunHighGrayLevelEmphasisSliceMerged2D    LongRunLowGrayLevelEmphasisSliceMerged2D    LongRunHighGrayLevelEmphasisSliceMerged2D    GrayLevelNonUniformitySliceMerged2D    NormalisedGrayLevelNonUniformitySliceMerged2D    RunLengthNonUniformitySliceMerged2D    NormalisedRunLengthNonUniformitySliceMerged2D    RunPercentageSliceMerged2D    GrayLevelVarianceSliceMerged2D    RunLengthVarianceSliceMerged2D    RunEntropySliceMerged2D
    _______    ______________________    ______________________    _______________________    ______________________    ___________________________    ____________________________    ___________________________    ____________________    _____________________    ____________________    _____________________________    __________________    _______________________    ___________________________    _____________________________________    _________________________________    ___________________________________________    _________________________    _____________________    _________________________    _________________________    ______________________    ___________________________    _________________________________    _________________________________    _________________________    _________________________    __________________________    _________________________    ______________________________    _______________________________    ______________________________    _______________________    ________________________    _______________________    ________________________________    _____________________    __________________________    ______________________________    ________________________________________    ____________________________________    ______________________________________________    ____________________________    ________________________    ____________________________    ____________________________    _________________________    ______________________________    ____________________________________    ____________________________________    ___________________________    __________________________    _________________________________    __________________________________    ______________________________________    _______________________________________    _____________________________________    ______________________________________    ________________________________    __________________________________________    ________________________________    __________________________________________    _______________________    ___________________________    ___________________________    ____________________    ______________________________    _____________________________    ____________________________________    _____________________________________    _________________________________________    __________________________________________    ________________________________________    _________________________________________    ___________________________________    _____________________________________________    ___________________________________    _____________________________________________    __________________________    ______________________________    ______________________________    _______________________

      "1"             0.025292                   10.263                    9.9482                     6.9415                      2.6834                          4.353                          2.8719                      20.525                   27.92                   4.3458                     0.011452                     11.872                  2.6834                       0.39071                             0.8891                                 0.3042                                    0.97287                               0.30871                    0.39971                    111.04                        27.92                     -17.682                      2401.8                           -0.10434                              0.66674                         0.021298                      10.262                        9.9491                       7.1238                          2.678                             4.5887                             2.9277                        20.525                      27.962                     4.3981                        0.0098741                       11.835                      2.678                          0.39129                                 0.8893                                    0.3049                                      0.97295                                  0.30908                       0.40201                        111.06                          27.962                        -18.094                         2408.5                             -0.052692                                0.55415                             0.91083                        1.4376                          0.023078                               119.32                                 0.021376                                   108.52                                   0.030767                                   173.59                                 75.51                                   0.087674                                  710.23                                  0.79414                               0.896                       12.076                         0.15199                     4.243                       0.91189                           1.4316                              0.023109                                 119.34                                    0.021486                                       108.64                                      0.030465                                     172.87                                    301.76                                     0.087414                                     2836.1                                      0.79414                                 0.896                           12.083                           0.15192                        4.2827         

[1] Vallières, Martin, Carolyn R. Freeman, Sonia R. Skamene, and Issam El Naqa. “A Radiomics Model from Joint FDG-PET and MRI Texture Features for the Prediction of Lung Metastases in Soft-Tissue Sarcomas of the Extremities.” The Cancer Imaging Archive, 2015. https://doi.org/10.7937/K9/TCIA.2015.7GO2GSKS.

[2] Vallières, M, C R Freeman, S R Skamene, and I El Naqa. “A Radiomics Model from Joint FDG-PET and MRI Texture Features for the Prediction of Lung Metastases in Soft-Tissue Sarcomas of the Extremities.” Physics in Medicine and Biology 60, no. 14 (July 7, 2015): 5471–96. https://doi.org/10.1088/0031-9155/60/14/5471.

[3] Clark, Kenneth, Bruce Vendt, Kirk Smith, John Freymann, Justin Kirby, Paul Koppel, Stephen Moore, et al. “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository.” Journal of Digital Imaging 26, no. 6 (December 2013): 1045–57. https://doi.org/10.1007/s10278-013-9622-7.

Input Arguments

R — Data and ROI for feature computation

radiomics object

Data and ROI for feature computation, specified as a radiomics object. The radiomics object R contains details of the preprocessed data and region of interest (ROI) from which to compute the features.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: textureFeatures(R,Type="GLCM",SubType="2D") computes the GLCM texture features as the average over 2-D slices in the 2-D resampled data.

Type — Category of texture features

"all" (default) | string scalar | character vector | string array | cell array of character vectors

Category of texture features to compute, specified as one or more of these options.

• "GLCM" — Grey level co-occurrence matrix

• "GLRLM" — Grey level run length matrix

• "GLSZM" — Grey level size zone matrix

• "GLDZM" — Grey level distance zone matrix

• "NGTDM" — Neighbourhood grey tone difference matrix

• "NGLDM" — Neighbourhood grey level dependence matrix

• "all"

If you specify "all", the function computes every category of texture features. For more information on which specific texture features each category includes, see IBSI Standard and Radiomics Function Feature Correspondences.

Data Types: char | string

SubType — Resampling from which to compute texture features

"2D" | "2.5D" | "3D" | "all"

Resampling from which to compute texture features, specified as one of these options.

• "2D" — Computes features for each 2-D slice in the 2-D resampled data and averages them. This is the default option for 2-D data, when the Data and ROILabel properties of the radiomics object R are 2-D matrices or medicalImage objects.

• "2.5D" — Computes features after merging all 2-D slices in the 2-D resampled data.

• "3D" — Computes features for the entire 3-D volume in the 3-D resampled data. This is the default option for 3-D data, when the Data and ROILabel properties of the radiomics object R are 3-D arrays or medicalVolume objects. 3-D resampling is not applicable for 2-D data.

• "all" — Computes features for all applicable options.

For 3-D data, when you perform 3-D resampling of the volume, the function makes the voxel spacing along all three spatial dimensions isotropic. However, when you perform 2-D resampling of the volume, the function makes the voxel spacing along only the x- and y-dimensions isotropic, while retaining the voxel spacing of the input volume along the z-dimension.

Data Types: char | string

Output Arguments

T — Texture features

table

Texture features, returned as a table. The first column in T is LabelID. The subsequent columns are the texture features. Each row of the table corresponds to an ROI. For more details on which texture features are computed in each Type and SubType, see IBSI Standard and Radiomics Function Feature Correspondences.

Version History

Introduced in R2023b

R2025a: Compute texture features for multiple ROIs

Compute texture features for multiple regions of interest (ROIs) in a medical image or medical volume.

R2024b: Compute one or more types of texture features

Compute one or more types of texture features by specifying one or more options in the name-value argument Type.