Statistical morphological skull stripping of adult and infant MRI data

Authors:
John Chiverton;Kevin Wells;Emma Lewis;Chao Chen;Barbara Podda;Declan Johnson
Affiliations:
Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK;Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK;Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK;Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK;Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy;St. George's Hospital Medical School, Cranmer Terrace, London SW17 0RE, UK
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

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.