Statistical morphological skull stripping of adult and infant MRI data

J. Chiverton, Kevin Wells, Emma Lewis, Chao Chen, Barbara Podda, Declan Johnson

    Research output: Contribution to journalArticlepeer-review


    This paper describes a novel automatic statistical morphology skull stripper (SMSS) that uniquely exploits a statistical self-similarity measure and a 2-D brain mask to delineate the brain. The result of applying SMSS to 20 MRI data set volumes, including scans of both adult and infant subjects is also described. Quantitative performance assessment was undertaken with the use of brain masks provided by a brain segmentation expert. The performance is compared with an alternative technique known as brain extraction tool. The results suggest that SMSS is capable of skull-stripping neurological data with small amounts of over- and under-segmentation.
    Original languageEnglish
    Pages (from-to)342-357
    Number of pages16
    JournalComputers in Biology and Medicine
    Issue number3
    Publication statusPublished - 1 Mar 2007


    • Mathematical morphology
    • Image processing
    • Skull stripping
    • MRI
    • Neurological
    • Brain
    • Infant


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