Advertisement

Signal, Image and Video Processing

, Volume 13, Issue 5, pp 941–949 | Cite as

Sequential graph-based extraction of curvilinear structures

  • Shuaa S. Alharbi
  • Chris G. Willcocks
  • Philip T. G. Jackson
  • Haifa F. Alhasson
  • Boguslaw ObaraEmail author
Original Paper
  • 159 Downloads

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.

Keywords

Curvilinear structures Centreline enhancement Graph-based method Object detection 

Notes

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.

References

  1. 1.
    Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36(2), 81–121 (2004)CrossRefGoogle 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)CrossRefGoogle Scholar
  3. 3.
    Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 113–125 (1998)CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  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)MathSciNetzbMATHCrossRefGoogle 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)CrossRefGoogle Scholar
  19. 19.
    Willcocks, C.G., Li, F.W.: Feature-varying skeletonization. Vis. Comput. 28(6–8), 775–785 (2012)CrossRefGoogle 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)CrossRefGoogle 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)MathSciNetzbMATHCrossRefGoogle 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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)CrossRefGoogle 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)CrossRefGoogle 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)Google Scholar
  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)CrossRefGoogle Scholar
  33. 33.
    Sinclair, J.H., Stevens, B.J.: Circular DNA filaments from mouse mitochondria. Proc Natl Acad Sci 56(2), 508–514 (1966)CrossRefGoogle 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceDurham UniversityDurhamUK
  2. 2.Computer CollageQassim UniversityBuraydahKingdom of Saudi Arabia

Personalised recommendations