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Journal of Digital Imaging

, Volume 32, Issue 5, pp 779–792 | Cite as

Hybrid Airway Segmentation Using Multi-Scale Tubular Structure Filters and Texture Analysis on 3D Chest CT Scans

  • Minho Lee
  • June-Goo LeeEmail author
  • Namkug Kim
  • Joon Beom Seo
  • Sang Min Lee
Article

Abstract

Airway diseases are frequently related to morphological changes that may influence lung physiology. Accurate airway region segmentation may be useful for quantitative evaluation of disease prognosis and therapy efficacy. The information can also be applied to understand the fundamental mechanisms of various lung diseases. We present a hybrid method to automatically segment the airway regions on 3D volume chest computed tomography (CT) scans. This method uses multi-scale filtering and support vector machine (SVM) classification. The proposed scheme is comprised of two hybrid steps. First, a tubular structure-based multi-scale filter is applied to find the initial candidate airway regions. Second, for identifying candidate airway regions using the fuzzy connectedness technique, the small and disconnected branches of airway regions are detected using SVM classification trained to differentiate between airway and non-airway regions through texture analysis of user-defined landmark points. For development and evaluation of the method, two datasets were incorporated: (1) 55 lung-CT volumes from the Korean Obstructive Lung Disease (KOLD) Cohort Study and (2) 20 cases from the publicly open database (EXACT′09). The average tree-length detection rates of EXACT′09 and KOLD were 56.9 ± 11.0 and 70.5 ± 8.98, respectively. Comparison of the results for the EXACT′09 data set between the presented method and other methods revealed that our approach was a high performer. The method limitations were higher false-positive rates than those of the other methods and risk of leakage. In future studies, application of a convolutional neural network will help overcome these shortcomings.

Keywords

Airway segmentation Frangi filter Top-hat transform Support vector machine Hybrid filtering Fuzzy connectedness 

Notes

Funding Information

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2016R1C1B1011105) and a grant (2014-7006) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.

References

  1. 1.
    Porpodis K et al.: Pneumothorax and asthma. J Thorac Dis 6:S152, 2014Google Scholar
  2. 2.
    Kiraly AP, Higgins WE, McLennan G, Hoffman EA, Reinhardt JM: Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9:1153–1168, 2002CrossRefGoogle Scholar
  3. 3.
    Li B, Christensen GE, Hoffman EA, McLennan G, Reinhardt JM: Pulmonary CT image registration and warping for tracking tissue deformation during the respiratory cycle through 3D consistent image registration. Med Phys 35:5575–5583, 2008CrossRefGoogle Scholar
  4. 4.
    Chen B, Kitasaka T, Honma H, Takabatake H, Mori M, Natori H, Mori K: 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 7:465–482, 2012CrossRefGoogle Scholar
  5. 5.
    Hu S, Hoffman EA, Reinhardt JM: Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging 20:490–498, 2001CrossRefGoogle Scholar
  6. 6.
    Kuhnigk J-M, Hahn H, Hindennach M, Dicken V, Krass S, Peitgen H-O: Lung lobe segmentation by anatomy-guided 3 D watershed transform. Proc. Proceedings of SPIE: CityGoogle Scholar
  7. 7.
    Lee YK et al.: Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography. Lung 186:157–165, 2008CrossRefGoogle Scholar
  8. 8.
    Mori K, et al.: Lung lobe and segmental lobe extraction from 3D chest CT datasets based on figure decomposition and Voronoi division. Proc. Medical Imaging: CityGoogle Scholar
  9. 9.
    Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijanska A, Bauer C, Beichel R, Mendoza CS, Wiemker R, Lee J, Reeves AP, Born S, Weinheimer O, van Rikxoort EM, Tschirren J, Mori K, Odry B, Naidich DP, Hartmann I, Hoffman EA, Prokop M, Pedersen JH, de Bruijne M: Extraction of airways from CT (EXACT'09). IEEE Trans Med Imaging 31:2093–2107, 2012CrossRefGoogle Scholar
  10. 10.
    Aykac D, Hoffman EA, McLennan G, Reinhardt JM: Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images. IEEE Trans Med Imaging 22:940–950, 2003CrossRefGoogle Scholar
  11. 11.
    Mori K, Hasegawa J-I, Toriwaki J-I, Anno H, Katada K: Automated extraction and visualization of bronchus from 3D CT images of lung. Proc. Computer Vision, Virtual Reality and Robotics in Medicine: CityGoogle Scholar
  12. 12.
    Singh H, Crawford M, Curtin J, Zwiggelaar R: Automated 3D segmentation of the lung airway tree using gain-based region growing approach. Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention: CityGoogle Scholar
  13. 13.
    Sonka M, Park W, Hoffman EA: Rule-based detection of intrathoracic airway trees. IEEE Trans Med Imaging 15:314–326, 1996CrossRefGoogle Scholar
  14. 14.
    Kitasaka T, Mori K, Hasegawa J, Toriwaki J: 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, 2002Google Scholar
  15. 15.
    Tschirren J, Hoffman EA, McLennan G, Sonka M: Intrathoracic airway trees: Segmentation and airway morphology analysis from low-dose CT scans. IEEE Trans Med Imaging 24:1529–1539, 2005CrossRefGoogle Scholar
  16. 16.
    Feuerstein M, Kitasaka T, Mori K: Adaptive branch tracing and image sharpening for airway tree extraction in 3-D chest CT. Proc. Proc Second International Workshop on Pulmonary Image Analysis: CityGoogle Scholar
  17. 17.
    Schlathoelter T, Lorenz C, Carlsen IC, Renisch S, Deschamps T: Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy. Proc Medical Imaging 2002: CityGoogle Scholar
  18. 18.
    Lo P, Sporring J, Ashraf H, Pedersen JJ, de Bruijne M: Vessel-guided airway tree segmentation: A voxel classification approach. Med Image Anal 14:527–538, 2010CrossRefGoogle Scholar
  19. 19.
    Bauer C, Eberlein M, Beichel RR: Graph-based airway tree reconstruction from chest CT scans: evaluation of different features on five cohorts. IEEE Trans Med Imaging 34:1063–1076, 2015CrossRefGoogle Scholar
  20. 20.
    Lo P, de Bruijne M: Voxel classification based airway tree segmentation. Proc. Medical Imaging: CityGoogle Scholar
  21. 21.
    Yano H, Marco F, Kitasaka T, Mori K: Study on bronchus region extraction from 3D chest CT images using loca1 intensity structure analysis and CT value distribution feature. The institute of electronics information and communication, MI2009–13:69–74, 2009Google Scholar
  22. 22.
    Xu Z, Bagci U, Foster B, Mansoor A, Udupa JK, Mollura DJ: A hybrid method for airway segmentation and automated measurement of bronchial wall thickness on CT. Med Image Anal 24:1–17, 2015CrossRefGoogle Scholar
  23. 23.
    Meng Q, Kitasaka T, Nimura Y, Oda M, Ueno J, Mori K: Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume. Int J Comput Assist Radiol Surg:1–17, 2016Google Scholar
  24. 24.
    Chae EJ, Seo JB, Song JW, Kim N, Park BW, Lee YK, Oh YM, Lee SD, Lim SY: Slope of emphysema index: an objective descriptor of regional heterogeneity of emphysema and an independent determinant of pulmonary function. Am J Roentgenol 194:W248–W255, 2010CrossRefGoogle Scholar
  25. 25.
    Ballard DH: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn 13:111–122, 1981CrossRefGoogle Scholar
  26. 26.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA: Multiscale vessel enhancement filtering. Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention: CityGoogle Scholar
  27. 27.
    Serra J: Image analysis and mathematical morphology, v. 1. Academic press, 1982Google Scholar
  28. 28.
    Kong TY, Rosenfeld A: Topological algorithms for digital image processing. Elsevier, 1996Google Scholar
  29. 29.
    Kimmel R, Shaked D, Kiryati N, Bruckstein AM: Skeletonization via distance maps and level sets. Proc. Photonics for Industrial Applications: CityGoogle Scholar
  30. 30.
    Telea A, Vilanova A: A robust level-set algorithm for centerline extraction. Proc. Proceedings of the symposium on Data visualisation 2003: CityGoogle Scholar
  31. 31.
    Udupa JK, Samarasekera S: Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Models Image Process 58:246–261, 1996CrossRefGoogle Scholar
  32. 32.
    Chang Y, Lim J, Kim N, Seo JB, Lynch DA: A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier. Med Phys 40:051912, 2013CrossRefGoogle Scholar
  33. 33.
    Chabat F, Yang G-Z, Hansell DM: Obstructive lung diseases: Texture classification for differentiation at ct 1. Radiology 228:871–877, 2003CrossRefGoogle Scholar
  34. 34.
    Kim N, Seo JB, Lee Y, Lee JG, Kim SS, Kang S-H: Development of an automatic classification system for differentiation of obstructive lung disease using HRCT. J Digit Imaging 22:136–148, 2009CrossRefGoogle Scholar
  35. 35.
    Rudyanto RD, Kerkstra S, van Rikxoort EM, Fetita C, Brillet PY, Lefevre C, Xue W, Zhu X, Liang J, Öksüz İ, Ünay D, Kadipaşaogˇlu K, Estépar RSJ, Ross JC, Washko GR, Prieto JC, Hoyos MH, Orkisz M, Meine H, Hüllebrand M, Stöcker C, Mir FL, Naranjo V, Villanueva E, Staring M, Xiao C, Stoel BC, Fabijanska A, Smistad E, Elster AC, Lindseth F, Foruzan AH, Kiros R, Popuri K, Cobzas D, Jimenez-Carretero D, Santos A, Ledesma-Carbayo MJ, Helmberger M, Urschler M, Pienn M, Bosboom DGH, Campo A, Prokop M, de Jong PA, Ortiz-de-Solorzano C, Muñoz-Barrutia A, van Ginneken B: Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med Image Anal 18:1217–1232, 2014CrossRefGoogle Scholar
  36. 36.
    Xiao C, Staring M, Shamonin D, Reiber JH, Stolk J, Stoel BC: A strain energy filter for 3D vessel enhancement with application to pulmonary CT images. Med Image Anal 15:112–124, 2011CrossRefGoogle Scholar
  37. 37.
    Cortes C, Vapnik V: Support-vector networks. Mach Learn 20:273–297, 1995Google Scholar
  38. 38.
    Keshani M, Azimifar Z, Tajeripour F, Boostani R: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput Biol Med 43:287–300, 2013CrossRefGoogle Scholar
  39. 39.
    Smola AJ, Schölkopf B: Learning with kernels: Citeseer, 1998Google Scholar
  40. 40.
    Zheng S, Liu J, Tian JW: A new efficient SVM-based edge detection method. Pattern Recogn Lett 25:1143–1154, 2004 http://image.diku.dk/exact/exact_results.php CrossRefGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Biomedical Engineering Research Center, Asan Institute for Life SciencesAsan Medical CenterSeoulRepublic of Korea
  2. 2.Department of Convergence Medicine, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea
  3. 3.Department of Radiology and Research Institute of Radiology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulSouth Korea

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