Vessel segmentation using centerline constrained level set method | Multimedia Tools and Applications Skip to main content
Springer Nature Link
Log in

Vessel segmentation using centerline constrained level set method

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Abbreviations

CC-LS:

Centerline constrained level set

WHO:

World health organization

CV:

Chan-vase

RSF:

Region-scalable fitting

MPP-BT:

Minimal path propagation with back-tracking

CT:

Computer tomography

CTA:

Computer tomography angiography

CPU:

Central processing unit

qGPU:

Graphic processing unit

CUDA:

Compute unified device architecture

References

  1. Adalsteinsson D, Sethian J (1999) A fast level set method for propagating interfaces. J Comput Phys 118(2):269–277

    Article  MathSciNet  MATH  Google Scholar 

  2. Boskamp T et al (2004) New vessel analysis tool for morphometric quantification and visualization of vessels in CT and MR imaging data sets. Radiographics 24(1):287–297

    Article  Google Scholar 

  3. Brieva J, Gonzalez E, Gonzalez F et al. (2005) A level set method for vessel segmentation in coronary angiography. Conference: International Conference of the IEEE Engineering in Medicine & Biology Society IEEE Engineering in Medicine & Biology Society Conference PubMed: 6348–6351

  4. Cai W, Yoshida H (2006) Vesselness propagation: a fast interactive vessel segmentation method. Proc SPIE - The Int Soc Opt Eng 6144:6144

    Google Scholar 

  5. Caselles V, Catte F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66(1):1–31

    Article  MathSciNet  MATH  Google Scholar 

  6. Caselles V, Kimmel R, Sapiro G (1997) On geodesic active contours. Int J Comput Vis 22(1):61–79

    Article  MATH  Google Scholar 

  7. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(22):266–277

    Article  MATH  Google Scholar 

  8. Y Chen, Q Cao, et al. (2014) Centerline constrained minimal path propagation for vessel extraction. IEEE 11-th Int Sym Biomed Imag

  9. Chen Y et al (2016) Curve-like structure extraction using minimal path propagation with backtracking. IEEE Trans Image Process 25(2):988–1003

    Article  MathSciNet  MATH  Google Scholar 

  10. Chen L, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv: 1706.05587

  11. Chopp DL (1993) Computing minimal surfaces via level set curvature flow. J Comput Phys 106:77–91

    Article  MathSciNet  MATH  Google Scholar 

  12. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis:321–331

  13. Lesage D et al (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13(6):819

    Article  Google Scholar 

  14. Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. Proc IEEE Conf Comput Vis Pattern Recognit 1:430–436

    Google Scholar 

  15. Li C et al (2008) Minimization of region-scalable fitting energy for image segmentation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 17(10):1940–1949

    MathSciNet  MATH  Google Scholar 

  16. Li H, Yezzi A, Cohen L (2009) 3D multi-branch tubular surface and centerline extraction with 4D iterative key points International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin Heidelberg, pp 1042–1050

    Google Scholar 

  17. Li C et al (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process A Publ IEEE Sign Process Soc 19(10):3243

    MathSciNet  MATH  Google Scholar 

  18. Lorigo LM et al (2001) CURVES: curve evolution for vessel segmentation. Med Image Anal 5(3):195

    Article  Google Scholar 

  19. Malladi R, Sethian J, Vemuri B (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–174

    Article  Google Scholar 

  20. Nain D, Yezzi A, Turk G (2004) Vessel segmentation using a shape driven flow. Lect Notes Comput Sci 3216:51–59

    Article  Google Scholar 

  21. Osher S, Sethian J (1988) Fronts propagating with curvature dependent speed: algorithms based on the Hamilton-Jacobi formulation. J Comput Phys 79:12–49

    Article  MathSciNet  MATH  Google Scholar 

  22. Quek FKH, Kirbas C (2002) "simulated wave propagation and Traceback in vascular extraction," Engineering in Medicine and Biology, 2002. Conference and the Fall Meeting of the Biomedical Engineering Society Embs/bmes Conference, 2002. Proc Sec Joint 2:1078–1079

    Google Scholar 

  23. Rochery M, Jermyn I, Zerubia J (2005) Phase field models and higher-order active contours. Tenth IEEE Int Conf Comput Vision 2:970–976

    Article  Google Scholar 

  24. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. arXiv: 1505.04597

  25. Sum K, Cheung P (2008) Vessel extraction under non-uniform illumination: a level set approach. IEEE Trans Biomed Eng 55(1):358–360

    Article  Google Scholar 

  26. Tian Y, Chen Q, Wang W et al (2014) A vessel active contour model for vascular segmentation. Biomed Res Int

  27. Toledo R et al (2000) Tracking of elongated structures using statistical snakes. Conf Comput Vision Pattern Recogn 01:1157

    Google Scholar 

  28. Wang Y, Jiang H (2014) A nonparametric shape prior constrained active contour model for segmentation of coronaries in CTA images. Comput Math Methods Med

  29. Wang J, Zhao S, Liu Z et al (2016) An active contour model based on adaptive threshold for extraction of cerebral vascular structures. Computational and Mathematical Methods in Medicine

  30. Wesarg S, Firle EA (2004) Segmentation of vessels: the corkscrew algorithm. Proc SPIE - Int Soc Opt Eng 5370:1609–1620

    Google Scholar 

  31. Wink O, Niessen WJ, Viergever MA (2004) Multiscale vessel tracking. IEEE Trans Med Imaging 23(1):130–133

    Article  Google Scholar 

  32. World Health Organization (2013) WHO | the top 10 causes of death. Countries

  33. Xu C, Prince JL (Mar. 1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  MathSciNet  MATH  Google Scholar 

  34. Yan C, Zhang Y, Xu J et al (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  35. Yan C, Zhang Y, Xu J et al (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circ Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  36. Yan C, Xie H, Liu S et al (2018) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 19(1):220–229

    Article  Google Scholar 

  37. Yan C, Xie H, Chen J et al (2018) A fast Uyghur text detector for complex background images. IEEE Trans Multimedia 20(12):3389–3398

    Article  Google Scholar 

  38. Yan C, Xie H, Yang D et al (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295

    Article  Google Scholar 

  39. Yi J, Ra JB (2003) A locally adaptive region growing algorithm for vascular segmentation. Int J Imag Syst Technol 13(4):208–214

    Article  Google Scholar 

  40. Yim PJ, Choyke PL, Rakesh M (2001) Vessel surface reconstruction with a tubular deformable model. IEEE Trans Med Imaging 20(12):1411–1421

    Article  Google Scholar 

  41. Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: CVPR, pp 6230–6239

    Google Scholar 

Download references

Acknowledgements

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.

Funding

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.

Author information

Authors and Affiliations

  1. Laboratory of Image Science and Technology, the Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China

    Tianling Lv, Guanyu Yang, Yang Chen, Huazhong Shu & Limin Luo

  2. Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), Rennes, France

    Tianling Lv, Guanyu Yang, Yang Chen, Huazhong Shu & Limin Luo

  3. The School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210097, China

    Yudong Zhang

  4. Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education, Nanjing, China

    Jian Yang

Authors
  1. Tianling Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Guanyu Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yudong Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jian Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Yang Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Huazhong Shu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Limin Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yang Chen.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, T., Yang, G., Zhang, Y. et al. Vessel segmentation using centerline constrained level set method. Multimed Tools Appl 78, 17051–17075 (2019). https://doi.org/10.1007/s11042-018-7087-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7087-x

Keywords