Computer Recognition Systems 3 pp 389-396

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57) | Cite as

Reliable Airway Tree Segmentation Based on Hole Closing in Bronchial Walls

  • Michał Postolski
  • Marcin Janaszewski
  • Anna Fabijańska
  • Laurent Babout
  • Michel Couprie
  • Mariusz Jędrzejczyk
  • Ludomir Stefańczyk

Summary

Reliable segmentation of a human airway tree from volumetric computer tomography (CT) data sets is the most important step for further analysis in many clinical applications such as diagnosis of bronchial tree pathologies. In this paper the original airway segmentation algorithm based on discrete topology and geometry is presented. The proposed method is fully automated, reliable and takes advantage of well defined mathematical notions. Holes occur in bronchial walls due to many reasons, for example they are results of noise, image reconstruction artifacts, movement artifacts (heart beat) or partial volume effect (PVE). Holes are common problem in previously proposed methods because in some areas they can cause the segmentation algorithms to leak into surrounding parenchyma parts of a lung. The novelty of the approach consists in the application of a dedicated hole closing algorithm which closes all disturbing holes in a bronchial tree. Having all holes closed the fast region growing algorithm can be applied to make the final segmentation. The proposed method was applied to ten cases of 3D chest CT images. The experimental results showed that the method is reliable, works well in all cases and generate good quality and accurate results.

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References

  1. 1.
    Hong, L., et al.: 3D Virtual Colonoscopy. In: Loew, M., Gershon, N. (eds.) 1995 Biomedical Visualization, pp. 26–33 (1995)Google Scholar
  2. 2.
    Do Yeon, K., Jong Won, P.: Virtual angioscopy for diagnosis of carotid artery stenosis. Journal of KISS: Software and Applications 30(9-10), 821–828 (2003)Google Scholar
  3. 3.
    Perchet, D., Fetita, C.I., Preteux, F.: Advanced navigation tools for virtual bronchoscopy. Proceedings of the SPIE The International Society for Optical Engineering 5298(1), 147–58Google Scholar
  4. 4.
    Fatt, C.C., Kassim, I., Lo, C., Ng, I., Keong, K.C.: Volume Visualization for Surgical Planning System. Journal of Mechanics in Medicine and Biology (JMMB) 7(1), 55–63 (2007)CrossRefGoogle Scholar
  5. 5.
    Palágyi, K., Tschirren, J., Hoffman, E.A., Sonka, M.: Quantitative analysis of pulmonary airway tree structure. Computers in Biology and Medicine 36, 974–996 (2006)CrossRefGoogle Scholar
  6. 6.
    Mori, K., et al.: Automated extraction and visualization of bronchus from 3D CT images of lung. In: Ayache, N. (ed.) CVRMed 1995. LNCS, vol. 905, pp. 542–548. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  7. 7.
    Chiplunkar, R., Reinhardt, J.M., Hoffman, E.A.: Segmentation and quantitation of the primary human airway tree. SPIE Medical Imaging (1997)Google Scholar
  8. 8.
    Tozaki, T., Kawata, Y., Niki, N., et al.: Pulmonary Organs Analysis for Differential Diagnosis Based on Thoracic Thin-section CT Images. IEEE Transaction on Nuclear Science 45, 3075–3082 (1998)CrossRefGoogle Scholar
  9. 9.
    Law, T.Y., Heng, P.A.: Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing. In: SPIE Proceedings on Medical Imaging, pp. 906–916 (2000)Google Scholar
  10. 10.
    Pisupati, C., Wolf, L., Mitzner, W., Zerhouni, E.: Segmentation of 3D pulmonary trees using mathematical morphology. Mathematical morphology and its applications to image and signal processing, 409–416 (1996)Google Scholar
  11. 11.
    Preteux, F., Fetita, C.I., Grenier, P., Capderou, A.: Modeling, segmentation, and caliber estimation of bronchi in high-resolution computerized tomography. Journal of Electronic Imaging 8, 36–45 (1999)CrossRefGoogle Scholar
  12. 12.
    Bilgen, D.: Segmentation and analysis of the human aiway tree from 3D X-ray CT images. Master’s thesis (2000)Google Scholar
  13. 13.
    Park, W., Hoffman, E.A., Sonka, M.: Segmentation of intrathoracic airway trees: a fuzzy logic approach. IEEE Transactions on Medical Imaging 17, 489–497 (1998)CrossRefGoogle Scholar
  14. 14.
    Fetita, C.I., Preteux, F.: Quantitative 3D CT bronchography. In: Proceedings IEEE International Symposium on Biomedical Imaging, ISBI 2002 (2002)Google Scholar
  15. 15.
    Aktouf, Z., Bertrand, G., Perroton, L.: A three-dimensional holes closing algorithm. Pattern Recognition Letters 23(5), 523–531 (2002)CrossRefMATHGoogle Scholar
  16. 16.
    Tschirren, J., Hoffman, E.A., McLennan, G., Sonka, M.: Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis from Low-Dose CT Scans. IEEE Transactions on Medical Imaging 24(12), 1529–1539 (2005)CrossRefGoogle Scholar
  17. 17.
    Graham, M.W., Gibbs, J.D., Higgins, W.E.: Robust system for human airway-tree segmentation. In: Medical Imaging 2008: Image Processing. Proceedings of the SPIE, vol. 6914, pp. 69141J–69141J-18 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michał Postolski
    • 1
    • 2
  • Marcin Janaszewski
    • 1
    • 2
  • Anna Fabijańska
    • 1
  • Laurent Babout
    • 1
  • Michel Couprie
    • 3
  • Mariusz Jędrzejczyk
    • 4
  • Ludomir Stefańczyk
    • 4
  1. 1.Computer Engineering DepartmentTechnical University of ŁódźŁódźPoland
  2. 2.Department of Expert System and Artificial IntelligenceThe College of Computer Science in ŁódźŁódźPoland
  3. 3.Université Paris Est, LABINFO-IGM, A2SI-ESIEE 2, boulevard Blaise Pascal, Cité DESCARTESNoisy le Grand CEDEXFrance
  4. 4.Department of Radiology and Diagnostic ImagingMedical University of LodzŁódźPoland

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