intensityFeatures
Description
Examples
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 intensity features for both ROIs.
I = intensityFeatures(R)
I=2×50 table
"1" 2.3092e+03 2.4966e+03 2.3144e+03 6.9224e+04 0.9158 0.3904 2250 1744 2036 2715 3310 332 1566 208.8567 143.3701 201.9787 0.1137 0.0724 1.5284e+10 2.3293e+03 57.6709 696.6411 0.9149 0.3903 51 1 30 98 157
"2" 2.3496e+03 2.3553e+03 2.4385e+03 1.3586e+05 -0.7283 -0.0536 2500 1374 1933 2847 3145 534 1771 301.5276 217.8525 298.1146 0.1512 0.1082 2.3464e+10 2.4662e+03 94.8709 1.0675e+03 -0.7300 -0.0498 100 1 50 131 157
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 local intensity features of the ROI in the 2-D resampled CT image volume.
I = intensityFeatures(R,Type=["LocalIntensity","IntensityHistogram"],SubType="2D")
I=1×26 table
LabelID LocalIntensityPeak2D GlobalIntensityPeak2D MeanDiscretisedIntensity2D DiscretisedIntensityVariance2D DiscretisedIntensitySkewness2D DiscretisedIntensityKurtosis2D MedianDiscretisedIntensity2D MinimumDiscretisedIntensity2D TenthDiscretisedIntensityPercentile2D NinetiethDiscretisedIntensityPercentile2D MaximumDiscretisedIntensity2D IntensityHistogramMode2D DiscretisedIntensityInterquartileRange2D DiscretisedIntensityRange2D IntensityHistogramMeanAbsoluteDeviation2D IntensityHistogramRobustMeanAbsoluteDeviation2D IntensityHistogramMedianAbsoluteDeviation2D IntensityHistogramCoeffcientOfVariation2D IntensityHistogramQuartileCoeffcientOfDispersion2D DiscretisedIntensityEntropy2D DiscretisedIntensityUniformity2D MaximumHistogramGradient2D MaximumHistogramGradientIntensity2D MinimumHistogramGradient2D MinimumHistogramGradientIntensity2D
_______ ____________________ _____________________ __________________________ ______________________________ ______________________________ ______________________________ ____________________________ _____________________________ _____________________________________ _________________________________________ _____________________________ ________________________ ________________________________________ ___________________________ _________________________________________ _______________________________________________ ___________________________________________ _________________________________________ __________________________________________________ _____________________________ ________________________________ __________________________ ___________________________________ __________________________ ___________________________________
"1" 71.689 78.084 10.488 10.846 -0.069 -0.1125 11 1 6 15 20 10 5 19 2.6421 2.0664 2.639 0.31401 0.2381 3.761 0.085523 351.5 7 -344 13
[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.
