Segmentation of human skull in MRI using statistical shape information from CT data.
OBJECTIVE
To automatically segment the skull from the MRI data using a model-based three-dimensional segmentation scheme.


METHODS
This study exploited the statistical anatomy extracted from the CT data of a group of subjects by means of constructing an active shape model of the skull surfaces. To construct a reliable shape model, a novel approach was proposed to optimize the automatic landmarking on the coupled surfaces (i.e., the skull vault) by minimizing the description length that incorporated local thickness information. This model was then used to locate the skull shape in MRI of a different group of patients.


RESULTS
Compared with performing landmarking separately on the coupled surfaces, the proposed landmarking method constructed models that had better generalization ability and specificity. The segmentation accuracies were measured by the Dice coefficient and the set difference, and compared with the method based on mathematical morphology operations.


CONCLUSIONS
The proposed approach using the active shape model based on the statistical skull anatomy presented in the head CT data contributes to more reliable segmentation of the skull from MRI data.
Wang
Defeng
D
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Shi
Lin
L
Chu
Winnie C W
WC
Cheng
Jack C Y
JC
Heng
Pheng Ann
PA
D000293:Adolescent; D019412:Anatomy, Cross-Sectional / Q000379:methods; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D015203:Reproducibility of Results; D012886:Skull / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*