Automatic intracranial space segmentation for computed tomography brain images

C. Adamson, A.C. da Costa, R. Beare, A.G. Wood

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)563-571
Number of pages9
JournalJournal of Digital Imaging
Issue number3
Early online date6 Nov 2012
Publication statusPublished - Jun 2013


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