title: Automatic intracranial space segmentation for computed tomography brain images.
abstract: Craniofacial disorders are routinely diagnosed using computed tomography imaging. Corrective surgery is often performed early in life to restore the skull to a more normal shape. In order to quantitatively assess the shape change due to surgery, we present an automated method for intracranial space segmentation. The method utilizes a two-stage approach which firstly initializes the segmentation with a cascade of mathematical morphology operations. This segmentation is then refined with a level-set-based approach that ensures that low-contrast boundaries, where bone is absent, are completed smoothly. We demonstrate this method on a dataset of 43 images and show that the method produces consistent and accurate results.
authors.affiliation: Developmental Imaging, Murdoch Childrens Research Institute, Parkville, Melbourne, Australia.
authors.forename: C
authors.initials: C
authors.lastname: Adamson
authors.forename: A C
authors.initials: AC
authors.lastname: Da Costa
authors.forename: R
authors.initials: R
authors.lastname: Beare
authors.forename: A G
authors.initials: AG
authors.lastname: Wood
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D003398:Craniosynostoses / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D008297:Male; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*