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In recent years, there has been increasing interest in computing texture features from medical images for quantitative analysis called the "radiomics" approach [14].
Open source codes "KeyRes-Technologies" and "grayrlmatrix" under Matlab were used for computing texture parameters.
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Our texture model computes texture maps.
We derive several topographic features from the map and compute texture features based on wavelet decomposition.
Finally, a classifier trained with all computed texture descriptors is used to distinguish between images of healthy and unhealthy retinas with hemorrhages.
More specifically, the luminance and contrast similarity are computed in texture component, while the structural similarity is computed in edge component.
In digital image analysis, the distribution and spatial variation of pixels is computed using texture analysis in order to extract useful data.
In liver, computed tomography texture-based differentiation between normal tissue, benign tumors, and hepatocellular carcinoma has been demonstrated to be possible [15, 16].
Once the nine textural maps are computed, the final texture is estimated by weighted summation of the texture using Eq. (8).
Reflectance values of spectral bands, computed NDVI and texture measurements of Landsat 7 TM imagery were used as ancillary variables in all methods.
For spherical and cuboidal particles the local displacement gradients were used to compute the deformation texture by a Taylor type model.
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