Skip to main content

Advertisement

Log in

A monocentric centerline extraction method for ring-like blood vessels

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Centerline is generally used to measure topological and morphological parameters of blood vessels, which is pivotal for the quantitative analysis of vascular diseases. However, previous centerline extraction methods have two drawbacks on complex blood vessels, represented as the failure on ring-like structures and the existing of multi-voxel width. In this paper, we propose a monocentric centerline extraction method for ring-like blood vessels, which consists of three components. First, multiple centerlines are generated from the seed points that are chosen by randomly sprinkling points on blood vessel data. Second, multi-centerline fusion is used to repair the notches of centerlines on ring-like vessels, and the local maximum of distance from oundary is employed to remedy the missing centerline points. Finally, monocentric processing is devised to keep the vascular centerline with single voxel width. We compared the proposed method with Wan et al.’s method and topological thinning on five groups of data including synthesized vascular datasets and MR brain images. The result showed the proposed method performed better than the two contrast methods both by visual inspection and by quantitative assessment, which demonstrated the performance of the proposed method on ring-like blood vessels as well as the elimination of multi-voxel width points.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Antiga L (2002) Patient-specific modeling of geometry and blood flow in large arteries. PhD Dissertation. Politecnico di Milano

  2. Aylward S, Pizer S, Eberly D, Bullitt E (1996) Intensity ridge and widths for tubular object segmentation and description. In: Anon (ed) Proceedings of the workship on mathematical methods in biomedical image analysis. IEEE, San Francisco, CA, pp 131–138

  3. Bian Z, Tan W, Yang J, Liu J, Zhao D (2014) Accurate airway centerline extraction based on topological thinning using graph-theoretic analysis. Biomed Mater Eng 24:3239–3249

    PubMed  Google Scholar 

  4. Bitter I, Kaufman AE, Sato M (2001) Penalized-distance volumetric skeleton algorithm. IEEE Trans Vis Comput Graph 7:195–206

    Article  Google Scholar 

  5. Bullitt E, Zeng DL, Gerig G, Aylward S, Joshi S, Smith JK, Lin WL, Ewend MG (2005) Vessel tortuosity and brain tumor malignancy: a blinded study. Acad Radiol 12:1232–1240. https://doi.org/10.1016/j.acra.2005.05.027

    Article  PubMed  PubMed Central  Google Scholar 

  6. A C, D B GS (1985) A width-independent fast thinning algorithm. IEEE Trans Pattern Anal Mach Intell 7:463–474

    Google Scholar 

  7. Ćurić G (2014) Function of circle of Willis. J Cereb Blood Flow Metab 34:578–584

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ding M, Tong R, Liao SH, Dong J (2009) An extension to 3D topological thinning method based on LUT for colon centerline extraction. Comput Methods Prog in Biomed 94:39–47

    Article  CAS  Google Scholar 

  9. Elattar MA, Wiegerinck EM, Planken RN, Vanbavel E, van Assen HC, Baan J Jr, Marquering HA (2014) Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation. Med Biol Eng Comput 52:611–618. https://doi.org/10.1007/s11517-014-1165-7

    Article  CAS  PubMed  Google Scholar 

  10. Gray-Edwards HL, Salibi N, Josephson EM, Hudson JA, Cox NR, Randle AN, McCurdy VJ, Bradbury AM, Wilson DU, Beyers RJ et al (2014) High resolution MRI anatomy of the cat brain at 3 Tesla. J Neurosci Methods 227:10–17. https://doi.org/10.1016/j.jneumeth.2014.01.035

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hamarneh G, Jassi P (2010) VascuSynth: simulating vascular trees for generating volumetric image data with ground-truth segmentation and tree analysis. Comput Med Imaging Graph 34:605–616. https://doi.org/10.1016/j.compmedimag.2010.06.002

    Article  PubMed  Google Scholar 

  12. Hassouna MS, Farag AA (2005) Robust centerline extraction framework using level sets. IEEE Comput Soc Conf Comput Vis Pattern Recog 1: 458–465

  13. Heinzer S, Krucker T, Stampanoni M, Abela R, Meyer EP, Schuler A, Schneider P, Mueller R (2006) Hierarchical microimaging for multiscale analysis of large vascular networks. NeuroImage 32:626–636. https://doi.org/10.1016/j.neuroimage.2006.03.043

    Article  PubMed  Google Scholar 

  14. Heinzer S, Kuhn G, Krucker T, Meyer E, Ulmann-Schuler A, Stampanoni M, Gassmann M, Marti HH, Mueller R, Vogel J (2008) Novel three-dimensional analysis tool for vascular trees indicates complete micro-networks, not single capillaries, as the angiogenic endpoint in mice overexpressing human VEGF(165) in the brain. NeuroImage 39:1549–1558. https://doi.org/10.1016/j.neuroimage.2007.10.054

    Article  PubMed  Google Scholar 

  15. Hernández-Hoyos M, Orkisz M, Puech P, Mansard-Desbleds C, Douek P, Magnin IE (2002) Computer-assisted analysis of three-dimensional MR angiograms. Radiographics Rev Publ Radiol Soc North Am Inc 22:421–436

    Google Scholar 

  16. Huang A, Liu HM, Liu HM, Lee CW, Yang CY, Tsang YM, Tsang YM (2009) On concise 3-D simple point characterizations: a marching cubes paradigm. IEEE Trans Med Imaging 28:43–51

    Article  CAS  PubMed  Google Scholar 

  17. Jasika N, Alispahic N, Elma A, Ilvana K, Elma L, Nosovic N (2012) Dijkstra’s shortest path algorithm serial and parallel execution performance analysis. 2012 Proc 35th Int Convention MIPRO 2012: 1811-1815

  18. Jin D, Iyer KS, Chen C, Hoffman EA, Saha PK (2016) A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recognition Letters 76:32–40. https://doi.org/10.1016/j.patrec.2015.04.002

    Article  PubMed  Google Scholar 

  19. Kang DG, Suh DC, Ra JB (2009) Three-dimensional blood vessel quantification via centerline deformation. IEEE Trans Med Imaging 28:405–414

    Article  PubMed  Google Scholar 

  20. Krissian K, Malandain G, Ayache N (1998) Model based multiscale detection and reconstruction of 3D vessels. HAL - INRIA: RR-3442

  21. Krissian K, Malandain G, Ayache N (1998) Model based multiscale detection and reconstruction of 3D vessels. INRIA, City

  22. Kumar RP (2013) Study on liver blood vessel movement during breathing cycle. Colour Vis Comput Symp 8255:1–5

    Google Scholar 

  23. Kumar RP, Albregtsen F, Reimers M, Edwin B, Lango T, Elle OJ (2015) Three-dimensional blood vessel segmentation and centerline extraction based on two-dimensional cross-section analysis. Ann Biomed Eng 43:1223–1234. https://doi.org/10.1007/s10439-014-1184-4

    Article  PubMed  Google Scholar 

  24. Lahousse L, Tiemeier H, Ikram MA, Brusselle GG (2015) Chronic obstructive pulmonary disease and cerebrovascular disease: a comprehensive review. Respir Med 109:1371–1380. https://doi.org/10.1016/j.rmed.2015.07.014

    Article  PubMed  Google Scholar 

  25. Lam L, Lee SW, Suen CY (1992) Thinning methodologies—a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14:869–885

    Article  Google Scholar 

  26. Lee J, Kim G, Lee H, Shin BS, Shin YG (2008) Fast path planning in virtual colonoscopy. Comput Biol Med 38:1012–1023

    Article  PubMed  Google Scholar 

  27. Lee TC, Kashyap RL, Chu CN (1994) Building skeleton models via 3-D medial surface/axis thinning algorithms. Cvgip Graph Model Image Process 56:462–478

    Article  Google Scholar 

  28. Li H, Yezzi A (2007) Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Trans Med Imaging 26:1213–1223. https://doi.org/10.1109/tmi.2007.903696

    Article  PubMed  Google Scholar 

  29. Mendonça AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25:1200–1213

    Article  PubMed  Google Scholar 

  30. Mortier P, De Beule M, Van Loo D, Masschaele B, Verdonck P, Verhegghe B (2008) Automated generation of a finite element stent model. Med Biol Eng Comput 46:1169–1173. https://doi.org/10.1007/s11517-008-0410-3

    Article  PubMed  Google Scholar 

  31. Pagidipati NJ, Gaziano TA (2013) Estimating deaths from cardiovascular disease: a review of global methodologies of mortality measurement. Circulation 127:749–756. https://doi.org/10.1161/circulationaha.112.128413

    Article  PubMed  PubMed Central  Google Scholar 

  32. A SR, B E (2002) Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging 21:61–75

    Article  Google Scholar 

  33. Sadleir R, Whelan PF (2005) Colon centerline calculation for CT colonography using optimised 3D topological thinning. Comput Med Imaging Graph 29:251–258

    Article  PubMed  Google Scholar 

  34. Serrador JM, Picot PA, Rutt BK, Shoemaker JK, Bondar RL (2000) MRI measures of middle cerebral artery diameter in conscious humans during simulated orthostasis. Stroke 31:1672–1678

    Article  CAS  PubMed  Google Scholar 

  35. Tillich M, Hill BB, Paik DS, Petz K, Napel S, Zarins CK, Rubin GD (2001) Prediction of aortoiliac stent-graft length: comparison of measurement methods. Radiology 220:475–483

    Article  CAS  PubMed  Google Scholar 

  36. Wan M, Liang Z, Ke Q, Hong L, Bitter I, Kaufman AE (2002) Automatic centerline extraction for virtual colonoscopy. IEEE Trans Med Imaging 21:1450–1460

    Article  PubMed  Google Scholar 

  37. Xin L, Gao Z, Xiong H, Ghista D, Ren L, Zhang H, Wu W, Huang W, Hau WK (2016) Three-dimensional hemodynamics analysis of the circle of Willis in the patient-specific nonintegral arterial structures. Biomech Model Mechanobiol 15:1–18

    Article  Google Scholar 

  38. XuJ, Feng D, Wu J, Cui Z (2009) Robust centerline extraction for tree-like blood vessels based on the region growing algorithm and level-set method. 2009 Sixth Int Conf Fuzzy Syst Knowl Discov 4: 586–591 Doi https://doi.org/10.1109/FSKD.2009.916

  39. Yang G, Kitslaar P, Frenay M, Broersen A, Boogers MJ, Bax JJ, Reiber JHC, Dijkstra J (2012) Automatic centerline extraction of coronary arteries in coronary computed tomographic angiography. Int J Cardiovasc Imaging 28:921–933

    Article  PubMed  Google Scholar 

  40. Zhao F, Liang J, Chen D, Wang C, Yang X, Chen X, Cao F (2015) Automatic segmentation method for bone and blood vessel in murine hindlimb. Med Phys 42:4043–4054. https://doi.org/10.1118/1.4922200

    Article  PubMed  Google Scholar 

  41. Zhao F, Liang J, Chen X, Liu J, Chen D, Yang X, Tian J (2016) Quantitative analysis of vascular parameters for amicro-CT imaging of vascular networks with multi-resolution. Med Biol Eng Comput 54:511–524. https://doi.org/10.1007/s11517-015-1337-0

    Article  PubMed  Google Scholar 

  42. Zhao F, Liu J, Qu X, Xu X, Chen X, Yang X, Cao F, Liang J, Tian J (2014) In vivo quantitative evaluation of vascular parameters for angiogenesis based on sparse principal component analysis and aggregated boosted trees. Phys Med Biol 59:7777–7791

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 61601363, 61372046, 61401264, 11571012, 61640418, 81530058, and 61601154; the National Key R&D Program of China under Grant No. 2016YFC1300300; the Science and Technology Plan Program in Shaanxi Province of China under Grant Nos. 2013K12-20-12 and 2015KW-002; the Natural Science Research Plan Program in Shaanxi Province of China under Grant Nos. 2017JQ6017, 2015JM6322, and 2015JZ019; and the Scientific Research Foundation of Northwest University. The MR brain images from healthy volunteers used in this paper were collected and made available by the CASILab at The University of North Carolina at Chapel Hill and were distributed by the MIDAS Data Server at Kitware, Inc.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xiaowei He or Jimin Liang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All the MR brain data are obtained from public database. No human/animal experiments are involved in this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, F., Sun, F., Hou, Y. et al. A monocentric centerline extraction method for ring-like blood vessels. Med Biol Eng Comput 56, 695–707 (2018). https://doi.org/10.1007/s11517-017-1717-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-017-1717-8

Keywords

Navigation