Paper
10 March 2006 Level set based vertebra segmentation for the evaluation of Ankylosing Spondylitis
Sovira Tan, Jianhua Yao, Michael M. Ward M.D., Lawrence Yao M.D., Ronald M. Summers M.D.
Author Affiliations +
Abstract
Ankylosing Spondylitis is a disease of the vertebra where abnormal bone structures (syndesmophytes) grow at intervertebral disk spaces. Because this growth is so slow as to be undetectable on plain radiographs taken over years, it is necessary to resort to computerized techniques to complement qualitative human judgment with precise quantitative measures on 3-D CT images. Very fine segmentation of the vertebral body is required to capture the small structures caused by the pathology. We propose a segmentation algorithm based on a cascade of three level set stages and requiring no training or prior knowledge. First, the noise inside the vertebral body that often blocks the proper evolution of level set surfaces is attenuated by a sigmoid function whose parameters are determined automatically. The 1st level set (geodesic active contour) is designed to roughly segment the interior of the vertebra despite often highly inhomogeneous and even discontinuous boundaries. The result is used as an initial contour for the 2nd level set (Laplacian level set) that closely captures the inner boundary of the cortical bone. The last level set (reversed Laplacian level set) segments the outer boundary of the cortical bone and also corrects small flaws of the previous stage. We carried out extensive tests on 30 vertebrae (5 from each of 6 patients). Two medical experts scored the results at intervertebral disk spaces focusing on end plates and syndesmophytes. Only two minor segmentation errors at vertebral end plates were reported and two syndesmophytes were considered slightly under-segmented.
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Sovira Tan, Jianhua Yao, Michael M. Ward M.D., Lawrence Yao M.D., and Ronald M. Summers M.D. "Level set based vertebra segmentation for the evaluation of Ankylosing Spondylitis", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614407 (10 March 2006); https://doi.org/10.1117/12.652399
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Cited by 9 scholarly publications.
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KEYWORDS
Image segmentation

Bone

Image processing algorithms and systems

Computed tomography

Image processing

3D image processing

Neodymium

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