Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1. D. Zaas, R. Wise, C. Wiener, and Longcope Spirometry Investigation Team, “Airway obstruction is common but unsuspected in patients admitted to a general medicine service,” Chest 125(1), 106111 (2004).
3.Estimates for chronic obstructive pulmonary disease, asthma, pneumonia/influenza and other lung diseases are from Chart Book, 2007, National Heart, Lung and Blood Institute (2007).
4. M. Sonka and J. M. Fitzpatrick, (editors), Handbook of Medical Imaging: Medical Image Processing and Analysis (SPIE, 2000), Vol. 2, 1250 pp.
5. S. Matsuoka, Y. Kurihara, K. Yagihashi, M. Hoshino, and Y. Nakajima, “Airway dimensions at inspiratory and expiratory multisection CT in chronic obstructive pulmonary disease: correlation with airflow limitation,” Radiology 248(3), 10421049 (2008).
6. C. I. Panhuysen, E. R. Bleecker, G. H. Koeter, D. A. Meyers, and D. S. Postma, “Characterization of obstructive airway disease in family members of probands with asthma. An algorithm for the diagnosis of asthma,” Am. J. Respir. Crit. Care Med. 157(6 Pt 1), 17341742 (1998).
7. D. A. Schwartz, “Gene-environment interactions and airway disease in children,” Pediatrics 123(Suppl. 3), S151S159 (2009).
8. J. D. Blanchard, “Aerosol bolus dispersion and aerosol-derived airway morphometry: assessment of lung pathology and response to therapy, Part 1,” J. Aerosol Med. 9(2), 183205 (1996).
9. W. J. Kim, E. K. Silverman, E. Hoffman, G. J. Criner, Z. Mosenifar, F. C. Sciurba, B. J. Make, V. Carey, R. S. Estépar, A. Diaz, J. J. Reilly, F. J. Martinez, G. R. Washko, and NETT Research Group, “CT metrics of airway disease and emphysema in severe COPD,” Chest 136(2), 396404 (2009).
10. J. G. Goldin, “Quantitative CT of emphysema and the airways,” J. Thorac. Imaging 19(4), 235240 (2004).
11. T. B. Grydeland, A. Dirksen, H. O. Coxson, T. M. Eagan, E. Thorsen, S. G. Pillai, S. Sharma, G. E. Eide, A. Gulsvik, and P. S. Bakke, “Quantitative computed tomography measures of emphysema and airway wall thickness are related to respiratory symptoms,” Am. J. Respir. Crit. Care Med. 181(4), 353359 (2010).
12. M. Sonka, W. Park, and E. A. Hoffman, “Rule-based detection of intrathoracic airway trees,” IEEE Trans. Med. Imaging 15(3), 314326 (1996).
13. X. Artaechevarria, D. Pérez-Martín, M. Ceresa, G. de Biurrun, D. Blanco, L. M. Montuenga, B. van Ginneken, C. Ortiz-de-Solorzano, and A. Muñoz-Barrutia, “Airway segmentation and analysis for the study of mouse models of lung disease using micro-CT,” Phys. Med. Biol. 54(22), 70097024 (2009).
14. T. Schlathölter, C. Lorenz, I. C. Carlsen, S. Renisch, and T. Deschamps, “Simultaneous segmentation and tree reconstruction of the airways for virtual bronchoscopy,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2002), pp. 103113.
15. K. Mori, J. Hasegawa, J. Toriwaki, J. Anno, and K. Katada, “Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system,” in Proceeding of 13th International Conference on Pattern Recognition (ICPR’1996) (IEEE, Vienna, Austria, 1996), Vol. 3, pp. 528532.
16. M. Nakamura, S. Wada, T. Miki, Y. Shimada, Y. Suda, and G. Tamura, “Automated segmentation and morphometric analysis of the human airway tree from multidetector CT images,” J. Physiol. Sci. 58(7), 493498 (2008).
17. D. Aykac, E. A. Hoffman, G. McLennan, and J. M. Reinhardt, “Segmentation and analysis of the human airway tree from three-dimensional X-ray CT images,” IEEE Trans. Med. Imaging 22(8), 940950 (2003).
18. T. Y. Law and P. A. Heng, “Automated extraction of bronchus from 3D CT images of lung based on genetic algorithm and 3D region growing,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2000), pp. 906916.
19. D. Bartz, D. Mayer, J. Fischer, S. Ley, A. del Rio, S. Thust, C. P. Heussel, H. U. Kauczor, and W. Strasser, “Hybrid segmentation and exploration of the human lungs,” in IEEE Visualization (IEEE, Washington, DC, 2003), pp. 177184.
20. M. W. Graham, J. D. Gibbs, D. C. Cornish, and W. E. Higgins, “Robust 3-D airway tree segmentation for image-guided peripheral bronchoscopy,” IEEE Trans. Med. Imaging 29(4), 982997 (2010).
21. W. Park, E. A. Hoffman, and M. Sonka, “Segmentation of intrathoracic airway trees: a fuzzy logic approach,” IEEE Trans. Med. Imaging 17(4), 489497 (1998).
22. A. P. Kiraly, W. E. Higgins, G. McLennan, E. A. Hoffman, and J. M. Reinhardt, “Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy,” Acad. Radiol. 9, 11531168 (2002).
23. A. Fabijańska, “Two-pass region growing algorithm for segmenting airway tree from MDCT chest scans,” Comput. Med. Imaging Graph 33(7), 537546 (2009).
24. C. I. Fetita, F. Prêteux, C. Beigelman-Aubry, and P. Grenier, “Pulmonary airways: 3-D reconstruction from multislice CT and clinical investigation,” IEEE Trans. Med. Imaging 23(11), 13531364 (2004).
25. L. Fan and C. W. Chen, “Reconstruction of airway tree based on topology and morphological operations,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2000), pp. 4657.
26. T. Kitasaka, K. Mori, J. Hasegawa, and J. Toriwaki, “A method for extraction of bronchus regions from 3D chest X-ray CT images by analyzing structural features of the bronchus,” Lect. Notes Comput. Sci. 2879/2003, 603610 (2003).
27. D. Mayer, D. Bartz, J. Fischer, S. Ley, A. del Río, S. Thust, H. U. Kauczor, and C. P. Heussel, “Hybrid segmentation and virtual bronchoscopy based on CT images,” Acad. Radiol. 11(5), 551565 (2004).
28. J. Tschirren, E. A. Hoffman, G. McLennan, and M. Sonka, “Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans,” IEEE Trans. Med. Imaging 24(12), 15291539 (2005).
29. T. Bülow, C. Lorenz, and S. Renisch, “A general framework for tree segmentation and reconstruction from medical volume data,” in Proceedings of the Medical Image Computing and Computer-Assisted Intervention—Miccai 2004, Pt. 1 (Springer, Saint-Malo, France, 2004), Vol. 3216, pp. 533540.
30. W. B. van Ginneken, W. Baggerman, and E. M. van Rikxoort, “Robust segmentation and anatomical labeling of the airway tree from thoracic CT Scans,” Med Image Comput Comput Assist Interv. 11(pt 1), pp. 219226 (2008).
31. P. Lo and M. de Bruijne, “Voxel classification based airway tree segmentation,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2008), pp. 69141K69141K.
32. P. Lo, J. Sporring, J. J. Pedersen, and M. de Bruijne, “Airway tree extraction with locally optimal paths,” Med. Image Comput. Comput. Assist. Interv. 12(Pt 2), 5158 (2009).
33. P. Lo, J. Sporring, H. Ashraf, J. J. Pedersen, and M. de Bruijne, “Vessel-guided airway tree segmentation: A voxel classification approach,” Med. Image Anal. 14(4), 527538 (2010).
34. Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans.Med. Phys. 30(8), 20402051 (2003).
35. Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143168 (1998).
36. K. Krissiana, G. Malandaina, N. Ayachea, R. Vaillantb, and Y. Troussetb, “Model-based detection of tubular structures in 3D images,” Comput. Vis. Image Understand. 80, 130171 (2000).
37. C. Bauer and H. Bischof, “A novel approach for detection of tubular objects and its application to medical image analysis,” Lect. Notes Comput. Sci. 5096/2008, 163172 (2008).
38. J. Pu, C. Fuhrman, W. F. Good, F. C. Sciurba, and D. Gur, “A differential geometric approach to automated segmentation of human airway tree,” IEEE Trans. Med. Imaging 30(2), 266278 (2011).
39. P. Lo, J. M. Reinhardt, and M. de Bruijne, “Extraction of Airways from CT (EXACT’09),” in Proceedings of the 2nd International Workshop on Pulmonary Image Analysis (CreateSpace, London, UK, 2009), pp. 175189.
40. K. Mori, J. Hasegawa, Y. Suenaga, and J. Toriwaki, “Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system,” IEEE Trans. Med. Imaging 19(2), 103114 (2000).
41. J. Tschirren, G. McLennan, K. Palágyi, E. A. Hoffman, and M. Sonka, “Matching and anatomical labeling of human airway tree,” IEEE Trans. Med. Imaging 24(12), 15401547 (2005).
42. H. Kitaoka, Y. Park, J. Tschirren, J. Reinhardt, M. Sonka, G. McLennan, and E. A. Hoffman, “Automated nomenclature labeling of the bronchial tree in 3D-CT lung images,” Lect. Notes Comput. Sci. 2489/2002, 111 (2002).
43. O. K. Au, C. L. Tai, H. K. Chu, D. Cohen-Or, and T. Y. Lee, “Skeleton extraction by mesh contraction,” ACM Trans. Graph. 27(3), 110 (2008).
44. I. Bitter, A. E. Kaufman, and M. Sato, “Penalized-distance volumetric skeleton algorithm,” IEEE Trans. Vis. Comput. Graph. 7(3), 195206 (2001).
45. N. D. Cornea, D. Silver, and P. Min, “Curve-skeleton properties, applications, and algorithms,” IEEE Trans. Vis. Comput. Graph. 13(3), 530548 (2007).
46. A. Sharf, T. Lewiner, A. Shamir, and L. Kobbelt, “On-the-fly curve-skeleton computation for 3D shapes,” Comput. Graph. Forum 26(3), 323328 (2007).
47. C. M. Ma and M. Sonka, “A fully parallel 3D thinning algorithm and its applications,” Comput. Vis. Image Understand. 64, 420433 (1996).
48. A. Chaturvedi and Z. Lee, “Three-dimensional segmentation and skeletonization to build an airway tree data structure for small animals,” Phys. Med. Biol. 50(7), 14051419 (2005).
49. R. D. Swift, A. P. Kiraly, A. J. Sherbondy, A. L. Austin, E. A. Hoffman, G. McLennan, and W. E. Higgins, “Automatic axis generation for virtual bronchoscopic assessment of major airway obstructions,” Comput. Med. Imaging Graph. 26(2), 103118 (2002).
50. L. Pisupati, W. Mitzner, and E. A. Zerhouni, “Geometric tree matching with applications to 3D lung structures,” in Proceedings of the Symposium on Computational Geometry (ACM, Philadelphia, PA, 1996), pp. 1920.
51. M. Pelillo, K. Siddiqi, and S. W. Zucker, “Matching hierarchical structures using association graphs,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 11051120 (1999).
52. M. Bartoli, M. Pelillo, K. Siddiqi, and S. W. Zucker, “Attributed tree homomorphism using association graphs,” in Proceedings of the IEEE International Conference on Pattern Recognition (IEEE, Barcelona, Spain, 2000), pp. 21332136.
53. J. Kawai, S. Saita, M. Kubo, Y. Kawata, N. Niki, Y. Nakano, H. Nishitani, H. Ohmatsu, K. Eguchi, and N. Moriyama, “Automated anatomical labeling algorithm of bronchial branches based on multi-slice CT images,” in SPIE Medical Imaging (SPIE, Lake Buena Vista, FL, 2006), pp. 907914.
54. Z. A. Aziz, A. U. Wells, S. R. Desai, S. M. Ellis, A. E. Walker, S. MacDonald, and D. M. Hansell, “Functional impairment in emphysema: contribution of airway abnormalities and distribution of parenchymal disease,” AJR Am. J. Roentgenol. 185(6), 15091515 (2005).
55. P. Berger, V. Perot, P. Desbarats, J. M. Tunon-de-Lara, R. Marthan, and F. Laurent, “Airway wall thickness in cigarette smokers: quantitative thin-section CT assessment,” Radiology 235(3), 10551064 (2005).
56.U.S. National Heart Lung and Blood Institute, “What is COPD,”
57. S. A. Wood, E. A. Zerhouni, J. D. Hoford, E. A. Hoffman, and W. Mitzner, “Measurement of three-dimensional lung tree structures by using computed tomography,” J. Appl. Physiol. 79(5), 16871697 (1995).
58. S. Matsuoka, K. Uchiyama, H. Shima, N. Ueno, S. Oish, and Y. Nojiri, “Bronchoarterial ratio and bronchial wall thickness on high-resolution CT in asymptomatic subjects: correlation with age and smoking,” AJR Am. J. Roentgenol. 180(2), 513518 (2003).
59. P. A. de Jong, F. R. Long, J. C. Wong, P. J. Merkus, H. A. Tiddens, J. C. Hogg, and H. O. Coxson, “Computed tomographic estimation of lung dimensions throughout the growth period,” Eur. Respir. J. 27(2), 261267 (2006).
60. R. Wiemker, T. Blaffert, T. Bülow, S. Renisch, and C. Lorenz, “Automated assessment of bronchial lumen, wall thickness and bronchoarterial diameter ratio of the tracheobronchial tree using high-resolution CT,” Int. Congr. Ser. 1268, 967972 (2004).
61. I. Orlandi, C. Moroni, G. Camiciottoli, M. Bartolucci, M. Pistolesi, N. Villari, M. Mascalchi, “Chronic obstructive pulmonary disease: thin-section CT measurement of airway wall thickness and lung attenuation,” Radiology 234(2), 604610 (2005).
62. T. Achenbach, O. Weinheimer, A. Biedermann, S. Schmitt, D. Freudenstein, E. Goutham, R. P. Kunz, R. Buhl, C. Dueber, and C. P. Heussel, “MDCT assessment of airway wall thickness in COPD patients using a new method: correlations with pulmonary function tests,” Eur. Radiol. 18(12), 27312738 (2008).
63. J. C. Hogg, F. Chu, S. Utokaparch, R. Woods, W. M. Elliott, L. Buzatu, R. M. Cherniack, R. M. Rogers, F. C. Sciurba, H. O. Coxson, and P. D. Paré, “The nature of small-airway obstruction in chronic obstructive pulmonary disease,” N. Engl. J. Med. 350(26), 26452653 (2004).
64. W. R. Webb, G. Gamsu, S. D. Wall, C. E. Cann, and E. Proctor, “CT of a bronchial phantom. Factors affecting appearance and size measurements,” Invest. Radiol. 19(5), 394398 (1984).
65. A. A. Bankier, D. Fleischmann, R. Mallek, A. Windisch, F. W. Winkelbauer, M. Kontrus, L. Havelec, C. J. Herold, and P. Hübsch, “Bronchial wall thickness: appropriate window settings for thin-section CT and radiologic-anatomic correlation,” Radiology 199(3), 831836 (1996).
66. M. Okazawa, N. Müller, A. E. McNamara, S. Child, L. Verburgt, and P. D. Paré, “Human airway narrowing measured using high resolution computed tomography,” Am. J. Respir. Crit. Care Med. 154(5), 15571562 (1996).
67. Y. Nakano, S. Muro, H. Sakai, T. Hirai, K. Chin, M. Tsukino, K. Nishimura, H. Itoh, P. D. Paré, J. C. Hogg, and M. Mishima, “Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function,” Am. J. Respir. Crit. Care Med. 162(3 Pt 1), 11021108 (2000).
68. Y. Nakano, S. E. Kalloger, H. O. Coxson, and P. D. Pare, “Development and validation of human airway analysis algorithm using multidetector row CT,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2002), pp. 460469.
69. J. M. Reinhardt, N. D. D’Souza, and E. A. Hoffman, “Accurate measurement of intrathoracic airways,” IEEE Trans. Med. Imaging 16(6), 820827 (1997).
70. G. G. King, N. L. Müller, K. P. Whittall, Q. S. Xiang, and P. D. Paré, “An analysis algorithm for measuring airway lumen and wall areas from high-resolution computed tomographic data,” Am. J. Respir. Crit. Care Med. 161(2 Pt 1), 574580 (2000).
71. O. I. Saba, E. A. Hoffman, and J. M. Reinhardt, “Maximizing quantitative accuracy of lung airway lumen and wall measures obtained from X-ray CT imaging,” J. Appl. Physiol. 95(3), 10631075 (2003).
72. R. Estépar, G. Washko, E. Silverman, J. Reilly, R. Kikinis, and C. F. Westin, “Accurate Airway Wall Estimation Using Phase Congruency,” Medical Image Computing and Computer-Assisted Intervention—Miccai 2006, Pt 2 (Springer, Copenhagen, Denmark, 2006), Vol. 4191, pp. 125134.
73. A. Saragaglia, C. Frita, F. Preteux, P. Brillet, and P. Grenier, “Accurate 3D quantification of the bronchial parameters in MDCT,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2005), pp. 323334.
74. M. Ortner, C. Fetita, P. Brillet, F. Preteux, and P. Grenier, “3D vector flow guided segmentation of airway wall in MSCT,” in Proceedings of the 6th International Conference on Advances in Visual Computing (Springer, Las Vegas, NV, 2010), pp. 302311.
75. T. L. Bauer and K. V. Steiner, “Virtual bronchoscopy: clinical applications and limitations,” Surg. Oncol. Clin. N. Am. 16(2), 323328 (2007).
76. J. S. Ferguson and G. McLennan, “Virtual bronchoscopy,” Proc. Am. Thorac. Soc. 2(6), 488491504505 (2005).
77. W. E. Higgins, K. Ramaswamy, R. D. Swift, G. McLennan, and E. A. Hoffman, “Virtual bronchoscopy for three-dimensional pulmonary image assessment: State of the art and future needs,” Radiographics 18(3), 761778 (1998).
78. B. J. Wood and P. RazaviVirtual endoscopy: a promising new technology,” Am. Fam. Physician 66(1), 107112 (2002).
79. W. De Wever, J. Bogaert, and J. A. Verschakelen, “Virtual bronchoscopy: Accuracy and usefulness—An overview,” Semin. Ultrasound CT MR 26(5), 364373 (2005).
80. K. Dheda, C. M. Roberts, M. R. Partridge, and I. Mootoosamy, “Is virtual bronchoscopy useful for physicians practising in a district general hospital?,” Postgrad. Med. J. 80(945), 420423 (2004).
81. S. E. Finkelstein, R. M. Summers, D. M. Nguyen, J. H. Stewart IV, J. A. Tretler, and D. S. Schrump, “Virtual bronchoscopy for evaluation of malignant tumors of the thorax,” J. Thorac. Cardiovasc. Surg. 123(5), 967972 (2002).
82. M. Haliloglu, A. O. Ciftci, A. Oto, B. Gumus, F. C. Tanyel, M. E. Senocak, N. Buyukpamukcu, and A. Besim, “CT virtual bronchoscopy in the evaluation of children with suspected foreign body aspiration,” Eur J Radiol. 48(2), 188192 (2003).
83. M. D. Seemann, K. Gebicke, W. Luboldt, J. M. Albes, J. Vollmar, J. F. Schäfer, T. Beinert, K. H. Englmeier, M. Bitzer, C. D. Claussen, “Hybrid rendering of the chest and surface-based virtual bronchoscopy in the operative and interventional therapy control,” Rofo 173(7), 650657 (2001).
84. P. A. de Jong, N. L. Müller, P. D. Paré, and H. O. Coxson, “Computed tomographic imaging of the airways: relationship to structure and function,” Eur. Respir. J. 26(1), 140152 (2005).
85. M. Hasegawa, Y. Nasuhara, Y. Onodera, H. Makita, K. Nagai, S. Fuke, Y. Ito, T. Betsuyaku, and M. Nishimura, “Airflow limitation and airway dimensions in chronic obstructive pulmonary disease,” Am. J. Respir. Crit. Care Med. 173(12), 13091315 (2006).
86. S. B. Fain, J. C. Granroth, J. D. Newell, S. E. Wenzell, D. S. Gierada, M. Castro, E. A. Hoffman, “Variability of quantitative CT airway measures of remodeling,” Am. J. Respir. Crit. Care Med. 179, A5575 (2009).
87. J. P. Williamson, A. L. James, M. J. Phillips, D. D. Sampson, D. R. Hillman, and P. R. Eastwood, “Quantifying tracheobronchial tree dimensions: methods, limitations and emerging techniques,” Eur. Respir. J. 34(1), 4255 (2009).
88. H. Arakawa, K. Fujimoto, Y. Fukushima, and Y. Kaji, “Thin-section CT imaging that correlates with pulmonary function tests in obstructive airway disease,” Eur. J. Radiol. 80(2), e157e163 (2011).
89. J. K. Leader, C. R. Fuhrman, J. Tedrow, S. C. Park, J. Tan, J. Pu, J. M. Drescher, D. Gur, and F. C. Sciurba, “Association between lung function and airway wall density,” in SPIE Medical Imaging (SPIE, Lake Buena Vista, FL, 2009), pp. 2J12J9.
90. P. Bokov, B. Mauroy, M. P. Revel, P. A. Brun, C. Peiffer, C. Daniel, M. M. Nay, B. Mahut, and C. Delclaux, “Lumen areas and homothety factor influence airway resistance in COPD,” Respir. Physiol. Neurobiol. 173(1), 110 (2010).
91. A. A. Diaz, C. Valim, T. Yamashiro, R. S. Estépar, J. C. Ross, S. Matsuoka, B. Bartholmai, H. Hatabu, E. K. Silverman, and G. R. Washko, “Airway count and emphysema assessed by chest CT imaging predicts clinical outcome in smokers,” Chest 138(4), 880887 (2010).
92. J. K. Leader, F. C. Sciurba, C. R. Fuhrman, J. M. Bon, S. C. Park, J. Pu, and D. Gur, “The relation of airway size to lung function,” in SPIE Medical Imaging (SPIE, San Diego, CA, 2008), pp. 231238.
93. T. Handa, S. Nagai, T. Hirai, K. Chin, T. Kubo, T. Oga, A. Niimi, H. Matsumoto, Y. Ito, K. Takahashi, K. Watanabe, T. Izumi, and M. Mishima, “Computed tomography analysis of airway dimensions and lung density in patients with sarcoidosis,” Respiration 77(3), 273281 (2009).
94. H. O. Coxson, “Quantitative computed tomography assessment of airway wall dimensions: current status and potential applications for phenotyping chronic obstructive pulmonary disease,” Proc. Am. Thorac. Soc. 5(9), 940945 (2008).

Data & Media loading...


Article metrics loading...



As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography(CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd