Airway Segmentation, Skeletonization, and Tree Matching to Improve Registration of 3D CT Images with Large Opacities in the Lungs

  • Duván Alberto Gómez Betancur
  • Anna Fabijańska
  • Leonardo Flórez-Valencia
  • Alfredo Morales Pinzón
  • Eduardo Enrique Dávila Serrano
  • Jean-Christophe Richard
  • Maciej Orkisz
  • Marcela Hernández Hoyos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9972)


In this work, we address the registration of pulmonary images, representing the same subject, with large opaque regions within the lungs, and with possibly large displacements. We propose a hybrid method combining alignment based on gray levels and landmarks within the same cost function. The landmarks are nodes of the airway tree obtained by specially developed segmentation and skeletonization algorithms. The former uses the random walker approach, whereas the latter exploits the minimum spanning tree constructed by the Dijkstra’s algorithm, in order to detect end-points and bifurcations. Airway trees from different images are matched by a modified best-first-search algorithm with a specially designed distance function. The proposed method was evaluated on computed-tomography images of subjects with acute respiratory distress syndrome, acquired at significantly different mechanical ventilation conditions. It achieved better results than registration based only on gray levels, but also better than hybrid registration using a standard airway-segmentation method.


Acute Respiratory Distress Syndrome Minimum Span Tree Oriented Graph Ventilation Condition Airway Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Colciencias for doctoral scholarships granted to D. Gómez Betancur and A. Morales Pinzón, and also for its support through the French-Colombian ECOS-NORD program C15M04 grant. This work was also supported by the French-Polish PHC Polonium 34852WG grant.


  1. 1.
    Cao, K., Ding, K., et al.: Improving intensity-based lung CT registration accuracy utilizing vascular information. Int. J. Biomed. Imaging 2012, 17 (2012). doi: 10.1155/2012/285136. Article ID 285136Google Scholar
  2. 2.
    Delmon, V., Rit, S., et al.: Registration of sliding objects using direction dependent B-splines decomposition. Phys. Med. Biol. 8(5), 1303–1314 (2013)CrossRefGoogle Scholar
  3. 3.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Fabijanska, A.: Segmentation of pulmonary vascular tree from 3D CT thorax scans. Biocybern. Biomed. Eng. 35(2), 106–119 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Feragen, A., Petersen, J., Owen, M., Lo, P., Hohwü Thomsen, L., et al.: Geodesic atlas-based labeling of anatomical trees: Application and evaluation on airways extracted from CT. IEEE Trans. Med. Imaging 34, 1212–1226 (2015)CrossRefGoogle Scholar
  6. 6.
    Flórez-Valencia, L., Morales Pinzón, A., et al.: Simultaneous skeletonization and graph description of airway trees in 3D CT images. In: Proceedings of the 25th GRETSI (2015)Google Scholar
  7. 7.
    Frangi, A.F., Niessen, W.J., et al.: Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans. Med. Imaging 18(10), 946–956 (1999)CrossRefGoogle Scholar
  8. 8.
    Graham, M.W., Higgins, W.E.: Optimal graph-theoretic approach to 3D anatomical tree matching. In: Proceedings of the 3rd ISBI, pp. 109–112 (2006)Google Scholar
  9. 9.
    Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  10. 10.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: elastix: a toolbox for intensity based medical image registration. IEEE Trans. Med. Imaging 29, 196–205 (2010)CrossRefGoogle Scholar
  11. 11.
    Lo, P., van Ginneken, B., Reinhardt, J.M., Tarunashree, Y., et al.: Extraction of airways from CT (EXACT 2009). IEEE Trans. Med. Imaging. 31, 2093–2107 (2012)CrossRefGoogle Scholar
  12. 12.
    Maurer, C.R., Qi, R., Raghavan, V.: A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 265–270 (2003)CrossRefGoogle Scholar
  13. 13.
    Metzen, J.H., Kröger, T., Schenk, A., et al.: Matching of anatomical tree structures for registration of medical images. Image Vis. Comput. 27, 923–933 (2009)CrossRefGoogle Scholar
  14. 14.
    Mori, K., Hasegawa, J., Toriwaki, J., Anno, H., Katada, K.: Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system. In: Proceedings of the 13th International Conference on Pattern Recognition, vol. 3, pp. 528–532 (1996)Google Scholar
  15. 15.
    Murphy, K., van Ginneken, B., Reinhardt, J.M., Kabus, S., Ding, K., Deng, X., et al.: Evaluation of registration methods on thoracic CT the EMPIRE10 challenge. IEEE Trans. Med. Imaging 30(11), 1901–1920 (2011)CrossRefGoogle Scholar
  16. 16.
    Polzin, T., Rühaak, J., Werner, R., Strehlow, J., Heldmann, S., et al.: Combining automatic landmark detection and variational methods for Lung CT registration. In: Proceedings of the MICCAI 5th International Workshop on Pulmonary Image Analysis, pp. 85–96 (2013)Google Scholar
  17. 17.
    Pu, J., Gu, S., Liu, S., Zhu, S., Wilson, D., et al.: CT based computerized identification and analysis of human airways: a review. Med. Phys. 39, 2603–2616 (2012)CrossRefGoogle Scholar
  18. 18.
    Saha, P.K., Borgefors, G., Sanniti di Baja, G.: A survey on skeletonization algorithms and their applications. Pattern Recogn. Lett. 76, 3–12 (2016)CrossRefGoogle Scholar
  19. 19.
    van Rikxoort, E.M., van Ginneken, B.: Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys. Med. Biol. 58, 187–220 (2013)CrossRefGoogle Scholar
  20. 20.
    Tschirren, J., Mclennan, G., Palagyi, K., et al.: Matching and anatomical labeling of human airway tree. IEEE Trans. Med. Imaging 24, 1540–1547 (2005)CrossRefGoogle Scholar
  21. 21.
    Verscheure, L., Peyrodie, L., Dewalle, A.S., Reyns, N., Betrouni, N., et al.: Three-dimensional skeletonization and symbolic description in vascular imaging: preliminary results. Int. J. Comput. Assist. Radiol. Surg. 8(2), 233–246 (2013)CrossRefGoogle Scholar
  22. 22.
    Yin, Y., Hoffman, E.A., Ding, K., Reinhardt, J.M., Lin, C.-L.: A cubic B-spline-based hybrid registration of lung CT images for a dynamic airway geometric model with large deformation. Phys. Med. Biol. 56, 203–218 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Duván Alberto Gómez Betancur
    • 1
  • Anna Fabijańska
    • 2
  • Leonardo Flórez-Valencia
    • 3
  • Alfredo Morales Pinzón
    • 1
    • 4
  • Eduardo Enrique Dávila Serrano
    • 4
  • Jean-Christophe Richard
    • 4
  • Maciej Orkisz
    • 4
  • Marcela Hernández Hoyos
    • 1
  1. 1.Systems and Computing Engineering Department, School of EngineeringUniversidad de Los AndesBogotáColombia
  2. 2.Institute of Applied Computer ScienceŁódź University of TechnologyŁódźPoland
  3. 3.Facultad de IngenieríaPontificia Universidad JaverianaBogotáColombia
  4. 4.Univ Lyon, INSA-Lyon, Université Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206LyonFrance

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