Statistical morphological skull stripping of adult and infant MRI data.
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.
Chiverton
John
J
Centre for Vision, Speech and Signal Processing, School of Electronics and Physical Sciences, University of Surrey, Surrey, UK. j.chiverton@surrey.ac.uk
Wells
Kevin
K
Lewis
Emma
E
Chen
Chao
C
Podda
Barbara
B
Johnson
Declan
D
D000328:Adult; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D005103:Expert Systems; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D015233:Models, Statistical; D012886:Skull / Q000033:anatomy & histology; D012984:Software