Vessel segmentation using centerline constrained level set method

  • Tianling Lv
  • Guanyu Yang
  • Yudong Zhang
  • Jian Yang
  • Yang ChenEmail author
  • Huazhong Shu
  • Limin Luo


Vascular related diseases have become one of the most common diseases with high mortality, high morbidity and high medical risk in the world. Level set is a kind of active contour model, and can be used to extract vessel structures. However, the applications of level set methods in vessel segmentation suffer from two problems. The first problem is the error caused by the false inclusion of some non-vessel structures. The second one is the sensitivity of the level set evolution to the initialization condition. In this paper, we propose an algorithm termed Centerline constrained level set (CC-LS) for vessel segmentation which utilizes centerline information to improve the evolution of level set. Using centerline information as the initial level set condition leads to improved evolution efficiency and extraction accuracy. Additionally, a new centerline modulated velocity term can be used in the level set evolution function to avoid the wrong inclusion of non-vessel structures. Performance of the proposed CC-LS algorithm is well validated using both 2D and 3D coronary images in different types. The proposed method is able to attain satisfactory results on both 2D and 3D coronary data.


Vessel segmentation Centerline Minimal path tracking Level set 



Centerline constrained level set


World health organization




Region-scalable fitting


Minimal path propagation with back-tracking


Computer tomography


Computer tomography angiography


Central processing unit


Graphic processing unit


Compute unified device architecture



We thank Cardiology Department of the University Hospital of Rennes and radiology Department of the First Hospital of Nanjing for providing us the image data.


This work was supported in part by the State’s Key Project of Research and Development Plan under Grant 2017YFA0104302, Grant 2017YFC0109202 and 2017YFC0107900, the National Natural Science Foundation under Grant 81530060 and 61871117, Natural Science Foundation of Jiangsu Province under Grant BK20150647 and by Science Technology Foundation of Zhejiang province under Grant 2015C33199.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratory of Image Science and Technology, the Key Laboratory of Computer Network and Information IntegrationSoutheast University, Ministry of EducationNanjingChina
  2. 2.Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs)RennesFrance
  3. 3.The School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  4. 4.Key Laboratory of Photoelectronic Imaging Technology and SystemMinistry of EducationNanjingChina

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