Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation

Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irr...

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Bibliographic Details
Published in:BioMed research international 2018-01, Vol.2018 (2018), p.1-12
Main Authors: Tan, Jindong, Zhang, Yinlong, Liang, Wei, Li, Yang
Format: Article
Language:English
Subjects:
Algorithms
Bone surgery
Boundaries
Computed tomography
Contours
CT imaging
Female
Histograms
Humans
Image processing
Image segmentation
Lumbar Vertebrae - diagnostic imaging
Male
Medical imaging
Methods
Models, Biological
Morphological filters
Morphology
Noise
Probabilistic models
Registration
Shape
Signal processing
Spine
Spine (lumbar)
Surgery
Tomography, X-Ray Computed
Vertebrae
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Summary:Vertebrae computed tomography (CT) image automatic segmentation is an essential step for Image-guided minimally invasive spine surgery. However, most of state-of-the-art methods still require human intervention due to the inherent limitations of vertebrae CT image, such as topological variation, irregular boundaries (double boundary, weak boundary), and image noise. Therefore, this paper intentionally designed an automatic global level set approach (AGLSA), which is capable of dealing with these issues for lumbar vertebrae CT image segmentation. Unlike the traditional level set methods, we firstly propose an automatically initialized level set function (AILSF) that comprises hybrid morphological filter (HMF) and Gaussian mixture model (GMM) to automatically generate a smooth initial contour which is precisely adjacent to the object boundary. Secondly, a regularized level set formulation is introduced to overcome the weak boundary leaking problem, which utilizes the region correlation of histograms inside and outside the level set contour as a global term. Ultimately, a gradient vector flow (GVF) based edge-stopping function is employed to guarantee a fast convergence rate of the level set evolution and to avoid level set function oversegmentation at the same time. Our proposed approach has been tested on 115 vertebrae CT volumes of various patients. Quantitative comparisons validate that our proposed AGLSA is more accurate in segmenting lumbar vertebrae CT images with irregular boundaries and more robust to various levels of salt-and-pepper noise.
ISSN:2314-6133
2314-6141
DOI:10.1155/2018/6319879