Skip to main content
Springer Nature Link
Log in

Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Airway segmentation plays an important role in analyzing chest computed tomography (CT) volumes for computerized lung cancer detection, emphysema diagnosis and pre- and intra-operative bronchoscope navigation. However, obtaining a complete 3D airway tree structure from a CT volume is quite a challenging task. Several researchers have proposed automated airway segmentation algorithms basically based on region growing and machine learning techniques. However, these methods fail to detect the peripheral bronchial branches, which results in a large amount of leakage. This paper presents a novel approach for more accurate extraction of the complex airway tree.

Methods

This proposed segmentation method is composed of three steps. First, Hessian analysis is utilized to enhance the tube-like structure in CT volumes; then, an adaptive multiscale cavity enhancement filter is employed to detect the cavity-like structure with different radii. In the second step, support vector machine learning will be utilized to remove the false positive (FP) regions from the result obtained in the previous step. Finally, the graph-cut algorithm is used to refine the candidate voxels to form an integrated airway tree.

Results

A test dataset including 50 standard-dose chest CT volumes was used for evaluating our proposed method. The average extraction rate was about 79.1 % with the significantly decreased FP rate.

Conclusion

A new method of airway segmentation based on local intensity structure and machine learning technique was developed. The method was shown to be feasible for airway segmentation in a computer-aided diagnosis system for a lung and bronchoscope guidance system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. World Health Organization (Feb 2015) Cancer, fact sheet N deg 297. http://www.who.int/mediacentre/factsheets/fs297/en/

  2. Kuhnigk JM, Hahn H, Hindennach M, Dicken V, Krass S, Peitgen HO (2003) Lung lobe segmentation by anatomy-guided 3D watershed transform. Proc SPIE Med Imaging 5032:1482–1490

    Article  Google Scholar 

  3. Mori K, Nakada Y, Kitasaka T, Suenaga Y, Takabatake H, Mori M, Natori H (2008) Lung lobe and segmental lobe extraction from 3D chest CT datasets based on figure decomposition and Voronoi division. Proc SPIE Med Imaging 6914:69144K-1–69144K-12

    Article  Google Scholar 

  4. Hu S, Hoffman E, Reinhardt J (2001) Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging 20(6):490–498

    Article  CAS  PubMed  Google Scholar 

  5. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 20(7):595–604

    Article  CAS  PubMed  Google Scholar 

  6. Chen B, Kitasaka T, Honma H, Takabatake H, Mori M, Natori H, Mori K (2012) Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images. Int J Comput Assist Radiol Surg 7(3):465–482

    Article  PubMed  Google Scholar 

  7. Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM (2008) Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration. Med Phys 35(12):5575–5583

    Article  PubMed  PubMed Central  Google Scholar 

  8. Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM (2002) Three dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9(10):1153–1168

    Article  PubMed  Google Scholar 

  9. Lo P, Ginneken B, Reinhardt J, Yavarna T, Jong P, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijanska A, Bauer C, Beichel R, Mendoza C, Wiemker R, Lee J, Reeves A, Born R, Weinheimer O, Rikxoort E, Tschirren J, Mori K, Odry B, Naidich D, Hartmann I, Hoffman E, Prokop M, Pedersen J, Bruijne M (2012) Extraction of airways from CT (EXACT’09). IEEE Trans Med Imaging 31(11):2093–2107

    Article  PubMed  Google Scholar 

  10. Mori K, Hasegawa J, Toriwaki J, Anno H, Kataba K (1995) Automated extraction and visualization of bronchus from 3D CT images of lung. Proc CVRMIed 95:542–548

    Google Scholar 

  11. Aykac D, Hoffman E, McLennan G, Reinhardt J (2003) Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images. IEEE Trans Med Imaging 22(8):940–950

    Article  PubMed  Google Scholar 

  12. Sonka M, Park W, Hoffman E (1996) Rule-based detection of intrathoracic airway trees. IEEE Trans Med Imaging 15(3):314–326

    Article  CAS  PubMed  Google Scholar 

  13. Singh H, Crawford M, Curtin JP, Zwiggelaar R (2004) Automated 3D segmentation of the lung airway tree using gain-based region growing approach. In: MICCAI. Lecture notes in computer science, pp 975–982

  14. Kitasaka T, Mori K, Hasegawa J, Toriwaki J (2002) A method for extraction of bronchus regions from 3D branch tracing and image sharpening for airway tree chest X-ray images by analyzing structural features of the bronchus. Forma 17:321–338

    Google Scholar 

  15. Tschirren J, Hoffman EA, McLennan G, Sonka M (2005) Intrathoracic airway trees: segmentation and airway morphology analysis from low dose CT scans. IEEE Trans Med Imaging 24(12):1529–1539

    Article  PubMed  PubMed Central  Google Scholar 

  16. Feuerstein M, Kitasaka T, Mori K (2009) Adaptive branch tracing and image sharpening for airway tree extraction in 3-D chest CT. In: Proceeding of 2nd international workshop on pulmonary image analysis, pp 273–284

  17. Schlathoelter T, Lorenz C, Carlsena IC, Renischa S, Deschamps T (2002) Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. Proc SPIE Med Imaging 4684:103–113

    Article  Google Scholar 

  18. Lo P, Sporring J, Ashraf H, Pedersen J, Bruijne M (2010) Vessel-guided airway tree segmentation: a voxel classification approach. Med Image Anal 14(4):527–538

    Article  PubMed  Google Scholar 

  19. Lo P, de Bruijne M (2008) Voxel classification based airway tree segmentation. Proc SPIE Med Imaging 6914:69141K

    Article  Google Scholar 

  20. Yano H, Feuerstein M, Kitasaka T, Mori K (2009) Study on bronchus region extraction from 3D chest CT images using loca1 intensity structure analysis and CT value distribution feature. In: IEICE, MI2009-13, pp 69–74

  21. Meng Q, Kitasaka T, Nimura Y, Oda M, Mori K (2015) A study on improvement of airway segmentation using hybrid method. In: The 3rd IAPR Asian conference on pattern recognition, pp 225–229

  22. Meng Q, Kitsaka T, Nimura Y, Oda M, Mori K (2016) Accurate airway segmentation based on intensity structure analysis and graphcut. In: Proceedings of SPIE, 9784, SPIE medical imaging: computer-aided diagnosis

  23. Pratt W (1991) Digital image processing, 2nd edn. Wiley, New York

    Google Scholar 

  24. Sato Y, Westin C, Bhalerao A, Nakajima S, Shiraga N, Tamura S (2000) Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans Vis Comput Graph 6(2):160–180

    Article  Google Scholar 

  25. Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R (1998) 3D multiscale line filter for segmentation and visialization of curvilinear structures in medical images. Med Image Anal 2(2):143–168

    Article  CAS  PubMed  Google Scholar 

  26. Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. Med Image Comput Comput Assist Interv (MICCAI) 1496:130–137

    Google Scholar 

  27. Krissian K, Malandain G, Ayache N (2000) Model based detection of tubular structures in 3D images. Comput Vis Image Underst 80(2):130–171

    Article  Google Scholar 

  28. Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two and three-dimensional CT images. Med Phys 30:20–40

    CAS  Google Scholar 

  29. Hirano Y, Xu R, Tachibana R, Kido S (2011) A method for extracting airway tree by using a cavity enhancement filter. In: 4th international workshop on pulmonary image analysis, pp 91–99

  30. Lesage D, Angelini ED, Bloch I, Funka-Lea G (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes. Med Image Anal 13(6):819–845

    Article  PubMed  Google Scholar 

  31. Chang C, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  32. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239

    Article  Google Scholar 

  33. Boykov Y, Kolmogorov V (2004) An experimental comparison of minexperimental comparison of minexperimental min-cutmax-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26(9):1124–1137

  34. Ali AM, El-Baz AS, Farag AA (2007) A novel framework for accurate lung segmentation using graph cuts. In: 4th IEEE international symposium on biomedical imaging, pp 908–911

Download references

Acknowledgments

The authors thank our colleagues for suggestions and advice. Parts of this research were supported by the MEXT, the JSPS KAKENHI Grant Numbers 25242047, 26108006, 26560255, and the Kayamori Foundation of Informational Science Advancement.

Author information

Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Qier Meng, Masahiro Oda & Kensaku Mori

  2. Graduate School of Information Science, Aichi Institute of Technology, 1247 Yachigusa, Yagusa-cho, Toyota, Aichi, Japan

    Takayuki Kitasaka

  3. Information Strategy Office, Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

    Yukitaka Nimura & Kensaku Mori

  4. Department of Diagnostic Radiology, Graduate School of Health Sciences, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8509, Japan

    Junji Ueno

Authors
  1. Qier Meng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Takayuki Kitasaka
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yukitaka Nimura
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Masahiro Oda
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Junji Ueno
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Kensaku Mori
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qier Meng.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, Q., Kitasaka, T., Nimura, Y. et al. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume. Int J CARS 12, 245–261 (2017). https://doi.org/10.1007/s11548-016-1492-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1492-2

Keywords