Sequential graph-based extraction of curvilinear structures

Abstract

In this paper, a new approach is proposed to extract an ordered sequence of curvilinear structures in images, capturing the largest and most influential paths first and then progressively extracting smaller paths until a prespecified size is reached. The results are demonstrated both quantitatively and qualitatively using synthetic and real-world images. The method is shown to outperform comparator methods for certain cases of noise, object class, and scale, while remaining fundamentally easier to use due to its low parameter requirement.

This is a preview of subscription content, access via your institution.

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

Notes

  1. 1.

    The software has been implemented in MATLAB and is made available at: https://github.com/ShuaaAlharbi/SGE.

References

  1. 1.

    Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)

    Article  Google Scholar 

  2. 2.

    Lam, B.S.Y., Yan, H.: A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans. Med. Imaging 27(2), 237–246 (2008)

    Article  Google Scholar 

  3. 3.

    Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)

    Article  Google Scholar 

  4. 4.

    Sironi, A., Türetken, E., Lepetit, V., Fua, P.: Multiscale centerline detection. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1327–1341 (2016)

    Article  Google Scholar 

  5. 5.

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image computing and computer Assisted Intervention, (Munich, Germany), pp. 234–241, Oct (2015)

  6. 6.

    Rouchdy, Y., Cohen, L.D.: Geodesic voting for the automatic extraction of tree structures, methods and applications. Comput. Vis. Image Underst. 117(10), 1453–1467 (2013)

    Article  Google Scholar 

  7. 7.

    Jin, D., Iyer, K.S., Chen, C., Hoffman, E.A., Saha, P.K.: A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recognit. Lett. 76(1), 32–40 (2016)

    Article  Google Scholar 

  8. 8.

    Bibiloni, P., González-Hidalgo, M., Massanet, S.: A survey on curvilinear object segmentation in multiple applications. Pattern Recognit. 60(1), 949–970 (2016)

    Article  Google Scholar 

  9. 9.

    Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images—a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012)

    Article  Google Scholar 

  10. 10.

    Leng, Z., Korenberg, J. R., Roysam, B., Tasdizen, T.: A rapid 2D centerline extraction method based on tensor voting. In: IEEE International Symposium on Biomedical Imaging: From Macro to Nano, (Chicago, IL), pp. 1000–1003, March (2011)

  11. 11.

    Smistad, E., Elster, A.C., Lindseth, F.: GPU-based airway segmentation and centerline extraction for image guided bronchoscopy. Norsk Informatikkonferanse 2012(1), 129–140 (2012)

    Google Scholar 

  12. 12.

    Bauer, C., Eberlein, M., Beichel, R.R.: Graph-based airway tree reconstruction from chest ct scans: evaluation of different features on five cohorts. IEEE Trans. Med. Imaging 34(5), 1063–1076 (2015)

    Article  Google Scholar 

  13. 13.

    Maragos, P., Schafer, R.: Morphological skeleton representation and coding of binary images. IEEE Trans. Acoust. Speech Signal Process. 34(5), 1228–1244 (1986)

    Article  Google Scholar 

  14. 14.

    Bai, X., Wang, T., Zhou, F.: Linear feature detection based on the multi-scale, multi-structuring element, grey-level hit-or-miss transform. Comput. Electr. Eng. 46(1), 487–499 (2015)

    Article  Google Scholar 

  15. 15.

    Shen, W., Bai, X., Hu, Z., Zhang, Z.: Multiple instance subspace learning via partial random projection tree for local reflection symmetry in natural images. Pattern Recognit. 52(1), 306–316 (2016)

    Article  Google Scholar 

  16. 16.

    Shen, W., Zhao, K., Jiang, Y., Wang, Y., Zhang, Z., Bai, X.: Object skeleton extraction in natural images by fusing scale-associated deep side outputs. In: IEEE Conference on Computer Vision and Pattern Recognition, (Las Vegas, Nevada), pp. 222–230, Jun (2016)

  17. 17.

    Shen, W., Zhao, K., Jiang, Y., Wang, Y., Bai, X., Yuille, A.: Deepskeleton: learning multi-task scale-associated deep side outputs for object skeleton extraction in natural images. IEEE Trans. Image Process. 26(11), 5298–5311 (2017)

    MathSciNet  MATH  Article  Google Scholar 

  18. 18.

    Chen, Y.-S., Hsu, W.-H.: A modified fast parallel algorithm for thinning digital patterns. Pattern Recognit. Lett. 7(2), 99–106 (1988)

    Article  Google Scholar 

  19. 19.

    Willcocks, C.G., Li, F.W.: Feature-varying skeletonization. Vis. Comput. 28(6–8), 775–785 (2012)

    Article  Google Scholar 

  20. 20.

    Lopez-Molina, C., de Ulzurrun, G.V.-D., Baetens, J.M., Van den Bulcke, J., De Baets, B.: Unsupervised ridge detection using second order anisotropic Gaussian kernels. Signal Process. 116(1), 55–67 (2015)

    Article  Google Scholar 

  21. 21.

    Helman, A.: The Finest Peaks: Prominence and Other Mountain Measures. Trafford Publishing, Victoria (2005)

    Google Scholar 

  22. 22.

    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    MathSciNet  MATH  Article  Google Scholar 

  23. 23.

    Chambon, S., Gourraud, C., Moliard, J.M., Nicolle, P.: Road crack extraction with adapted filtering and markov model-based segmentation: introduction and validation. In: International Joint Conference on Computer Vision Theory and Applications, (Angers, France), p. sp, May (2010)

  24. 24.

    Batool, N., Taheri, S., Chellappa, R.: Assessment of facial wrinkles as a soft biometrics. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, (Shanghai, China), pp. 1–7, April (2013)

  25. 25.

    Sharma, O., Mioc, D., Anton, F.: Voronoi diagram based automated skeleton extraction from colour scanned maps. In: International Symposium on Voronoi Diagrams in Science and Engineering, (Banff, Canada), pp. 186–195, Jul (2006)

  26. 26.

    Kerschnitzki, M., Kollmannsberger, P., Burghammer, M., Duda, G.N., Weinkamer, R., Wagermaier, W., Fratzl, P.: Architecture of the osteocyte network correlates with bone material quality. J. Bone Miner. Res. 28(8), 1837–1845 (2013)

    Article  Google Scholar 

  27. 27.

    Spirillum oil bacterium (2004). http://w3.marietta.edu/~spilatrs/biol202/labresults/spirillum_oil.jpg. Accessed 15 Feb 2017

  28. 28.

    Willcocks, C.G., Jackson, P.T., Nelson, C.J., Obara, B.: Extracting 3D parametric curves from 2D images of helical objects. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1757–1769 (2017)

    Article  Google Scholar 

  29. 29.

    Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Image Processing in Medical Imaging, (San Diego, CA), pp. 648–657, May (2004)

  30. 30.

    Treponema pallidum bacterium (2016). https://phil.cdc.gov/phil/details_linked.asp?pid=2333. Accessed 15 Feb 2017

  31. 31.

    Treponema bacteria (2004). http://w3.marietta.edu/~spilatrs/biol202/labresults/treponema.html. Accessed 15 Feb 2017

  32. 32.

    Griffith, J.D., Comeau, L., Rosenfield, S., Stansel, R.M., Bianchi, A., Moss, H., De Lange, T.: Mammalian telomeres end in a large duplex loop. Cell 97(4), 503–514 (1999)

    Article  Google Scholar 

  33. 33.

    Sinclair, J.H., Stevens, B.J.: Circular DNA filaments from mouse mitochondria. Proc Natl Acad Sci 56(2), 508–514 (1966)

    Article  Google Scholar 

  34. 34.

    Brown, K.M., Barrionuevo, G., Canty, A.J., De Paola, V., Hirsch, J.A., Jefferis, G.S., Lu, J., Snippe, M., Sugihara, I., Ascoli, G.A.: The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics 9(2–3), 143–157 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

Shuaa S. Alharbi and Haifa F. Alhasson are supported by the Saudi Arabian Ministry of Higher Education Doctoral Scholarship and Qassim University in Saudi Arabia.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Boguslaw Obara.

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

Verify currency and authenticity via CrossMark

Cite this article

Alharbi, S.S., Willcocks, C.G., Jackson, P.T.G. et al. Sequential graph-based extraction of curvilinear structures. SIViP 13, 941–949 (2019). https://doi.org/10.1007/s11760-019-01431-6

Download citation

Keywords

  • Curvilinear structures
  • Centreline enhancement
  • Graph-based method
  • Object detection