Fully automatic skull stripping of routine clinical neurological NMR data

Image analysis of neurological NMR data is often an easier undertaking when non-cerebral tissue compartment voxels are removed from the NMR image dataset. This preprocessing step is often called 'skull stripping'. The simple but robust technique formulated and presented in this paper utilizes a combination of mathematical morphology and statistical segmentation techniques. Non-tissue background voxels are deemed to possess a Rayleigh distribution and consequently removed using an adaptive region dividing technique. Further processing automatically identifies a set of voxels that act as a test slice to determine whether the cerebral tissue compartment voxels have been fully separated during subsequent morphological processing. This set is used as a test to terminate an iterative morphological processing scheme to disconnect cerebral from non-cerebral voxels. The method has been successfully applied to 9 NMR datasets of varying quality with low inter-slice resolution. It therefore appears that this approach should be sufficiently robust to be useful for the statistical analysis of routine clinical NMR data.