Fully automatic skull stripping of routine clinical neurological NMR data

John P. Chiverton, Barbara Podda, Chao Chen, Kevin Wells, Declan Johnson

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.
    Original languageEnglish
    Title of host publicationIEEE 2004 Nuclear science Symposium and Medical Imaging Conference Record
    Place of PublicationPiscataway
    PublisherIEEE
    Pages2669 - 2673
    Volume4
    ISBN (Print)9780780387007
    DOIs
    Publication statusPublished - 2004

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