Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

Amin Suzani; Abtin Rasoulian; Sidney Fels; Robert N. Rohling; Purang Abolmaesumi

doi:10.1117/12.2043847

12 March 2014 Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model

Amin Suzani, Abtin Rasoulian, Sidney Fels, Robert N. Rohling, Purang Abolmaesumi

Author Affiliations +

Proceedings Volume 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling; 90360P (2014) https://doi.org/10.1117/12.2043847
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Amin Suzani, Abtin Rasoulian, Sidney Fels, Robert N. Rohling, and Purang Abolmaesumi "Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90360P (12 March 2014); https://doi.org/10.1117/12.2043847

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