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Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis

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Abstract

The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. In this paper we present a novel, semi-automatic method for blood vessel segmentation and centerline extraction, by tracking the blood vessel tree from a user-initiated seed point to the ends of the blood vessel tree. The novelty of our method is in performing only two-dimensional cross-section analysis for segmentation of the connected blood vessels. The cross-section analysis is done by our novel single-scale or multi-scale circle enhancement filter, used at the blood vessel trunk or bifurcation, respectively. The method was validated for both synthetic and medical images. Our validation has shown that the cross-sectional centerline error for our method is below 0.8 pixels and the Dice coefficient for our segmentation is 80% ± 2.7%. On combining our method with an optional active contour post-processing, the Dice coefficient for the resulting segmentation is found to be 94% ± 2.4%. Furthermore, by restricting the image analysis to the regions of interest and converting most of the three-dimensional calculations to two-dimensional calculations, the processing was found to be more than 18 times faster than Frangi vesselness with thinning, 8 times faster than user-initiated active contour segmentation with thinning and 7 times faster than our previous method.

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References

  1. Abd-Almageed, W., C. E. Smith, and S. Ramadan. Kernel snakes: non-parametric active contour models, Vol. 1. IEEE International Conference on Systems, Man and Cybernetics, 2003. 2003, pp. 240–244.

  2. Albregtsen, F. Non-parametric histogram thresholding methods-error vs. relative object area. Proceedings of the Scandinavian Conference on Image Analysis. 1993, p. 12.

  3. Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8:679–698, 1986.

    Article  Google Scholar 

  4. Erdt, M., M. Raspe, and M. Suehling. Automatic hepatic vessel segmentation using graphics hardware. Medical Imaging and Augmented Reality. Springer, Berlin, 2008, pp. 403–412.

  5. Frangi, A. F., W. J. Niessen, K. L. Vincken, and M. A. Viergever. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Interventation—MICCAI’98. Springer, 1998, pp. 130–137.

  6. Galarreta-Valverde, M. A., M. M. Macedo, C. Mekkaoui, and M. P. Jackowski. Three-dimensional synthetic blood vessel generation using stochastic L-systems. SPIE Medical Imaging. 2013, pp. 86691I-1–86691I-6.

  7. Kapur, J. N., P. K. Sahoo, and A. K. C. Wong. A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graph. Image Process. 29:273–285, 1985.

    Article  Google Scholar 

  8. Kass, M., A. Witkin, and D. Terzopoulos. Snakes: active contour models. Int. J. Comput. Vis. 1:321–331, 1988.

    Article  Google Scholar 

  9. Kirbas, C., and F. K. Quek. Vessel extraction in medical images by 3D wave propagation and traceback. Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, 2003, pp. 174–181.

  10. Kirbas, C., and F. Quek. A review of vessel extraction techniques and algorithms. ACM Comput. Surv. CSUR 36:81–121, 2004.

    Article  Google Scholar 

  11. Krissian, K., G. Malandain, N. Ayache, R. Vaillant, and Y. Trousset. Model-based detection of tubular structures in 3D images. Comput. Vis. Image Underst. 80:130–171, 2000.

    Article  Google Scholar 

  12. Kumar, R. P., F. Albregtsen, M. Reimers, T. Langø, B. Edwin, and O. J. Elle. 3D multiscale vessel enhancement based centerline extraction of blood vessels. SPIE Medical Imaging. International Society for Optics and Photonics, 2013, pp. 86691X-1–86691X-9.

  13. Kumar, R. P., E.-J. Rijkhorst, O. Geier, D. Barratt, and O. J. Elle. Study on liver blood vessel movement during breathing cycle. Colour and Visual Computing Symposium (CVCS), IEEE, 2013, pp. 1–5.

  14. Lee, T. C., R. L. Kashyap, and C. N. Chu. Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP Graph. Models Image Process. 56:462–478, 1994.

    Article  Google Scholar 

  15. Lesage, D., E. D. Angelini, I. Bloch, and G. Funka-Lea. A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med. Image Anal. 13:819–845, 2009.

    Article  PubMed  Google Scholar 

  16. Lindeberg, T. Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21:225–270, 1994.

  17. Lorigo, L. M., O. D. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C.-F. Westin. CURVES: curve evolution for vessel segmentation. Med. Image Anal. 5:195–206, 2001.

    Article  CAS  PubMed  Google Scholar 

  18. Macedo, M. M. G., M. A. Galarreta-Valverde, C. Mekkaoui, and M. P. Jackowski. A centerline-based estimator of vessel bifurcations in angiography images. In: SPIE Medical Imaging, International Society for Optics and Photonics, edited by C. L. Novak and S. Aylward, 2013, pp. 86703K-1–86703K-7.

  19. Maksimovic, R., S. Stankovic, and D. Milovanovic. Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models—“snakes”. Int. J. Med. Inf. 58–59:29–37, 2000.

    Article  Google Scholar 

  20. Reinertsen, I., M. Descoteaux, K. Siddiqi, and D. L. Collins. Validation of vessel-based registration for correction of brain shift. Med. Image Anal. 11:374–388, 2007.

    Article  CAS  PubMed  Google Scholar 

  21. Sato, Y., S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Med. Image Anal. 2:143–168, 1998.

    Article  CAS  PubMed  Google Scholar 

  22. Schmitt, H., M. Grass, V. Rasche, O. Schramm, S. Haehnel, and K. Sartor. An X-ray-based method for the determination of the contrast agent propagation in 3-D vessel structures. IEEE Trans. Med. Imaging 21:251–262, 2002.

    Article  CAS  PubMed  Google Scholar 

  23. Yi, J., and J. B. Ra. A locally adaptive region growing algorithm for vascular segmentation. Int. J. Imaging Syst. Technol. 13:208–214, 2003.

    Article  Google Scholar 

  24. Yushkevich, P. A., J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, and G. Gerig. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31:1116–1128, 2006.

    Article  PubMed  Google Scholar 

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Acknowledgments

The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 238802 (IIIOS project) and also, received top-up financing from Norwegian Research Council. The authors thank Mr. Martin Rube and Prof. Andreas Melzer from the Institute of Medical Sciences and Technology, University of Dundee for providing the images necessary for our study. The authors also thank Mr. Rafael Palomar for proofreading the document.

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Authors and Affiliations

  1. The Intervention Centre, Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway

    Rahul Prasanna Kumar, Bjørn Edwin & Ole Jakob Elle

  2. Department of Informatics, University of Oslo, Gaustadalléen 23 B, 0373, Oslo, Norway

    Rahul Prasanna Kumar, Fritz Albregtsen, Martin Reimers & Ole Jakob Elle

  3. Institute for Cancer Genetics and Informatics, Oslo University Hospital, Sognsvannsveien 20, 0372, Oslo, Norway

    Fritz Albregtsen

  4. Faculty of Medicine, University of Oslo, Klaus Torgårds vei 3, 0372, Oslo, Norway

    Bjørn Edwin

  5. Medical Technology, SINTEF, Olav Kyrres Gate 9, MTFS, Trondheim, Norway

    Thomas Langø

Authors
  1. Rahul Prasanna Kumar
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  2. Fritz Albregtsen
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  3. Martin Reimers
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  4. Bjørn Edwin
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  5. Thomas Langø
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  6. Ole Jakob Elle
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Correspondence to Rahul Prasanna Kumar.

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Associate Editor Joel D. Stitzel oversaw the review of this article.

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Kumar, R.P., Albregtsen, F., Reimers, M. et al. Three-Dimensional Blood Vessel Segmentation and Centerline Extraction based on Two-Dimensional Cross-Section Analysis. Ann Biomed Eng 43, 1223–1234 (2015). https://doi.org/10.1007/s10439-014-1184-4

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  • DOI: https://doi.org/10.1007/s10439-014-1184-4

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