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Automatic intracranial space segmentation for computed tomography brain images
journal contribution
posted on 2013-06-01, 00:00 authored by C Adamson, A C Da Costa, R Beare, Amanda WoodAmanda WoodCraniofacial 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.
History
Journal
Journal of digital imagingVolume
26Issue
3Pagination
563 - 571Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0897-1889eISSN
1618-727XLanguage
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2012, Society for Imaging Informatics in MedicineUsage metrics
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