Proceedings Article | 15 March 2011
Proc. SPIE. 7962, Medical Imaging 2011: Image Processing
KEYWORDS: Principal component analysis, Statistical analysis, Detection and tracking algorithms, Data modeling, Image segmentation, Bone, Medical imaging, Computed tomography, Spine, Statistical modeling
An automatic segmentation algorithm for the vertebrae in human body CT images is presented. Especially we focused
on constructing and utilizing 4 different statistical intensity-shape combined models for the cervical, upper / lower
thoracic and lumbar vertebrae, respectively. For this purpose, two previously reported methods were combined: a
deformable model-based initial segmentation method and a statistical shape-intensity model-based precise segmentation
method. The former is used as a pre-processing to detect the position and orientation of each vertebra, which determines
the initial condition for the latter precise segmentation method. The precise segmentation method needs prior knowledge
on both the intensities and the shapes of the objects. After PCA analysis of such shape-intensity expressions obtained
from training image sets, vertebrae were parametrically modeled as a linear combination of the principal component
vectors. The segmentation of each target vertebra was performed as fitting of this parametric model to the target image
by maximum a posteriori estimation, combined with the geodesic active contour method. In the experimental result by
using 10 cases, the initial segmentation was successful in 6 cases and only partially failed in 4 cases (2 in the cervical
area and 2 in the lumbo-sacral). In the precise segmentation, the mean error distances were 2.078, 1.416, 0.777, 0.939
mm for cervical, upper and lower thoracic, lumbar spines, respectively. In conclusion, our automatic segmentation
algorithm for the vertebrae in human body CT images showed a fair performance for cervical, thoracic and lumbar
vertebrae.
