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Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation
Li Y(李杨)1,2; Liang W(梁炜)1; Zhang YL( 张吟龙)1,2; Tan JD(谈金东)3
作者部门工业控制网络与系统研究室
发表期刊BIOMED RESEARCH INTERNATIONAL
ISSN2314-6133
2018
页码1-12
收录类别SCI
WOS记录号WOS:000447894200001
产权排序1
资助机构National Science Foundation of China ; International Partnership Program of Chinese Academy of Sciences
摘要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.
语种英语
WOS类目Biotechnology & Applied Microbiology ; Medicine, Research & Experimental
关键词[WOS]GRADIENT VECTOR FLOW ; THEORETICAL FOUNDATIONS ; SPINE SEGMENTATION ; ACTIVE CONTOURS ; MODEL ; EVOLUTION ; SNAKES ; DRIVEN
WOS研究方向Biotechnology & Applied Microbiology ; Research & Experimental Medicine
资助项目National Science Foundation of China[61172145] ; National Science Foundation of China[61333019] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020]
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/23417
专题工业控制网络与系统研究室
通讯作者Liang W(梁炜)
作者单位1.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
推荐引用方式
GB/T 7714
Li Y,Liang W,Zhang YL,et al. Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation[J]. BIOMED RESEARCH INTERNATIONAL,2018:1-12.
APA Li Y,Liang W,Zhang YL,&Tan JD.(2018).Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation.BIOMED RESEARCH INTERNATIONAL,1-12.
MLA Li Y,et al."Automatic Global Level Set Approach for Lumbar Vertebrae CT Image Segmentation".BIOMED RESEARCH INTERNATIONAL (2018):1-12.
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