Steerable Features: It has been well documented in the literature that decomposition of images by using basis functions localized in spatial position, orientation, and scale (e.g., wavelets) are extremely useful in object recognition and detection [32] , [33] . Since steerable filters are rotation and translation invariant, they accurately represent the underlying image structure [34] . In this study, we use steerable derivative of Gaussian filters to decompose local regions around each candidate pattern into several oriented basis. These basis are used as features in voxelwise classification for TIB identification. We extract steerable features (i.e., directional derivatives) from one scale and six different orientations.