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Proceedings Paper

Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model
Author(s): Amin Suzani; Abtin Rasoulian; Sidney Fels; Robert N. Rohling; Purang Abolmaesumi
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Paper Abstract

Segmentation of vertebral structures in magnetic resonance (MR) images is challenging because of poor con­trast between bone surfaces and surrounding soft tissue. This paper describes a semi-automatic method for segmenting vertebral bodies in multi-slice MR images. In order to achieve a fast and reliable segmentation, the method takes advantage of the correlation between shape and pose of different vertebrae in the same patient by using a statistical multi-vertebrae anatomical shape+pose model. Given a set of MR images of the spine, we initially reduce the intensity inhomogeneity in the images by using an intensity-correction algorithm. Then a 3D anisotropic diffusion filter smooths the images. Afterwards, we extract edges from a relatively small region of the pre-processed image with a simple user interaction. Subsequently, an iterative Expectation Maximization tech­nique is used to register the statistical multi-vertebrae anatomical model to the extracted edge points in order to achieve a fast and reliable segmentation for lumbar vertebral bodies. We evaluate our method in terms of speed and accuracy by applying it to volumetric MR images of the spine acquired from nine patients. Quantitative and visual results demonstrate that the method is promising for segmentation of vertebral bodies in volumetric MR images.

Paper Details

Date Published: 12 March 2014
PDF: 6 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90360P (12 March 2014); doi: 10.1117/12.2043847
Show Author Affiliations
Amin Suzani, The Univ. of British Columbia (Canada)
Abtin Rasoulian, The Univ. of British Columbia (Canada)
Sidney Fels, The Univ. of British Columbia (Canada)
Robert N. Rohling, The Univ. of British Columbia (Canada)
Purang Abolmaesumi, The Univ. of British Columbia (Canada)


Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes, Editor(s)

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