selectFeatures

Selected radiomics features

Since R2025a

Syntax

F = selectFeatures(R,featureNames)

Description

F = selectFeatures(R,featureNames) computes the selected radiomics features featureNames for the radiomics object R.

Examples

Compute Selected 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 selected features for both ROIs.

F = selectFeatures(R,["MeanIntensity2D","ContrastAveraged2D"])

F=2×3 table
    LabelID    MeanIntensity2D    ContrastAveraged2D
    _______    _______________    __________________

      "1"          2314.4               122.85      
      "2"          2438.5               104.57      

Compute Selected 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 selected features of the ROI in the CT image volume.

I = selectFeatures(R,["Sphericity3D","IntensityHistogramMode3D","Busyness25D"])

I=1×4 table
    LabelID    Sphericity3D    IntensityHistogramMode3D    Busyness25D
    _______    ____________    ________________________    ___________

      "1"        0.80911                  10                 10.072   

[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.

featureNames — Names of features to compute

string scalar | character vector | string array | cell array of character vectors

Names of the features to compute, specified as a string scalar, character vector, string array, or cell array of character vectors. To compute a single feature, specify its name as a string scalar or a character vector. To compute multiple features, specify their names as a string array or a cell array of character vectors. The names of the features must match the exact spellings, including the suffix representing subtype, as specified in the Corresponding Feature columns of the tables on the IBSI Standard and Radiomics Function Feature Correspondences page.

Data Types: char | string

Output Arguments

F — Selected features

table

Selected features, returned as a table. The first column in F is LabelID. The subsequent columns are the selected features. Each row of the table corresponds to an ROI.

Version History

Introduced in R2025a