A Quantitative Study of Texture Features across Different Window Sizes in Prostate T2-weighted MRI

This study aims to investigate the effects of window size on the performance of prostate cancer CAD and to identify discriminant texture descriptors in prostate T2-W MRI. For this purpose we extracted 215 texture features from 418 T2-W MRI images and extracted them using 9 different window sizes (3 × 3 to 19 × 19). The Bayesian Network and Random Forest classifiers were employed to perform the classification. Experimental results suggest that using window size of 9 × 9 and 11 × 11 produced Az > 89%. Also, this study suggests a set of best texture features based on our experimental results.