  Automation of brain matter segmentation from MR images is a challenging task
due to the irregular boundaries between the grey and white matter regions. In
addition, the presence of intensity inhomogeneity in the MR images further
complicates the problem. In this paper, we propose a texture and vesselness
incorporated version of the ScatterNet Hybrid Deep Learning Network (TS-SHDL)
that extracts hierarchical invariant mid-level features, used by fisher vector
encoding and a conditional random field (CRF) to perform the desired
segmentation. The performance of the proposed network is evaluated by extensive
experimentation and comparison with the state-of-the-art methods on several 2D
MRI scans taken from the synthetic McGill Brain Web as well as on the MRBrainS
dataset of real 3D MRI scans. The advantages of the TS-SHDL network over
supervised deep learning networks is also presented in addition to its superior
performance over the state-of-the-art.
