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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
Department工业控制网络与系统研究室
Source PublicationBIOMED RESEARCH INTERNATIONAL
ISSN2314-6133
2018
Pages1-12
Indexed BySCI
WOS IDWOS:000447894200001
Contribution Rank1
Funding OrganizationNational Science Foundation of China ; International Partnership Program of Chinese Academy of Sciences
AbstractVertebrae 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.
Language英语
WOS SubjectBiotechnology & Applied Microbiology ; Medicine, Research & Experimental
WOS KeywordGRADIENT VECTOR FLOW ; THEORETICAL FOUNDATIONS ; SPINE SEGMENTATION ; ACTIVE CONTOURS ; MODEL ; EVOLUTION ; SNAKES ; DRIVEN
WOS Research AreaBiotechnology & Applied Microbiology ; Research & Experimental Medicine
Funding ProjectNational Science Foundation of China[61172145] ; National Science Foundation of China[61333019] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020]
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/23417
Collection工业控制网络与系统研究室
Corresponding AuthorLiang W(梁炜)
Affiliation1.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
Recommended Citation
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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