FIG. 9.

Performance of classifiers predicting glioblastoma subtypes using five texture feature descriptors extracted from scans of different image planes. The AUCs are averaged over all subtypes and both modalities (postcontrast T2-weighted and T1-weighted fluid-attenuated inversion recovery magnetic resonance imaging scans). The * sign on each bar indicates that the average AUC for that feature set if significantly higher than random classification (AUC = 0.5). Abbreviations: SFTA, segmentation-based fractal texture analysis; HOG, histogram of oriented gradients; RLM, run-length matrix; LBP, local binary patterns; and HARALICK, Haralick texture features.