TY - JOUR
T1 - Automatic intracranial space segmentation for computed tomography brain images
AU - Adamson, C.
AU - da Costa, A.C.
AU - Beare, R.
AU - Wood, A.G.
PY - 2013/6
Y1 - 2013/6
N2 - 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.
AB - 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.
UR - http://link.springer.com/article/10.1007%2Fs10278-012-9529-8
U2 - 10.1007/s10278-012-9529-8
DO - 10.1007/s10278-012-9529-8
M3 - Article
SN - 0897-1889
VL - 26
SP - 563
EP - 571
JO - Journal of Digital Imaging
JF - Journal of Digital Imaging
IS - 3
ER -