Table 1.

Summary of texture features used in this study as well as their significance for localization of CaP on T2w MRI (numbers in brackets signify how many features of each texture category were computed)

Feature	Implementation	Purpose	Significance for quantifying CaP appearance
Gabor wavelet transform (48)	Modulation of a complex sinusoid by a Gaussian function	Attempt to match localized frequency characteristics at multiple scales and orientations (26)	Quantify visual processing features used by radiologists when examining appearance of the carcinoma
Haar wavelet transform (12)	Decomposition coefficients via wavelet decomposition at multiple scales	Attempt decomposition of a signal in the discrete space while offering localization in the time and frequency domains (27)	Differentiate the amorphous nature of the non-CaP regions within foci of low SI
Haralick texture feature (36)	Construct joint probability distribution of the occurrence of greylevel intensities in an image (spatial relationship between pixels used to restrict counting of greylevel co-occurrences). Statistical features are then calculated from this distribution	Differentiate between different types of texture excellently due to calculation of 2nd order statistics (which quantify perceptual appearance of image) (25)	Useful in differentiating homogeneous low SI regions (CaP) from more hyper-intense appearance of normal prostate
Greylevel statistical features (14)	Mean, standard deviation as well as derivative features such as via convolution with the Sobel and Kirsch operators are calculated	Provide 1st order information, quantifying macroscopic appearance of image e.g. variation of intensities within image (24) etc.	May help localize regions of significant differences on T2w MR image, accurately detect region boundaries