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Table 2 A brief description of the intensity and radiomic features and their significance in quantifying the appearance of prostate cancer on MRI

From: Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

Feature category Description Significance
Signal intensity T2w, ADC signal intensity Regions of low signal intensities are indicative of prostate cancer lesions
First-order statistics Mean, standard deviation, median and range; first order differentials computed using Sobel operators Localize regions with significant intensity changes; Gradients detect edges and quantify region boundaries
Co-occurrence features A co-occurrence matrix is a compilation of the different spatial relationships of neighboring pixel intensities. Several metrics of this matrix are computed to obtain useful features, such as Haralick features Distinguish homogeneous regions of low intensities of cancer from hyper-intense normal regions
Gabor features A Gabor filter is a convolution of Gaussian function with a Fourier transform at different orientations and frequencies Gabor features quantify the appearance of cancer lesions at multiple orientations and image scales
Texture energy Laws’ texture energy features are computed by convolution of the image with local masks that are obtained from vectors which capture local average, edge, spot, wave and ripple patterns Texture energy quantifies the variation of pixel intensities within a fixed region of the image. Regions of image containing cancer lesions typically contain lower texture energy