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Proceedings Paper

Whole vertebral bone segmentation method with a statistical intensity-shape model based approach
Author(s): Shouhei Hanaoka; Karl Fritscher; Benedikt Schuler; Yoshitaka Masutani; Naoto Hayashi; Kuni Ohtomo; Rainer Schubert
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Paper Abstract

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.

Paper Details

Date Published: 14 March 2011
PDF: 14 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796242 (14 March 2011); doi: 10.1117/12.878151
Show Author Affiliations
Shouhei Hanaoka, The Health and Life Sciences Univ. (Austria)
The Univ. of Tokyo Hospital (Japan)
Karl Fritscher, The Health and Life Sciences Univ. (Austria)
Benedikt Schuler, The Health and Life Sciences Univ. (Austria)
Yoshitaka Masutani, The Univ. of Tokyo Hospital (Japan)
Naoto Hayashi, The Univ. of Tokyo Hospital (Japan)
Kuni Ohtomo, The Univ. of Tokyo Hospital (Japan)
Rainer Schubert, The Health and Life Sciences Univ. (Austria)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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