A generic approach to pathological lung segmentation

IEEE Transactions on Medical Imaging

Volumn 33, Issue 12, 2014, Pages 2293-2310

  Mansoor, Awais ^a   Bagci, Ulas ^a   Xu, Ziyue ^a   Foster, Brent ^a   Olivier, Kenneth N ^b   Elinoff, Jason M ^a   Suffredini, Anthony F ^a   Udupa, Jayaram K ^c   Mollura, Daniel J ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

^b NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES   (United States)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Anatomy guided segmentation; computed tomography; fuzzy connectedness; pathological lung segmentation; random forest

Indexed keywords

ADULT; AGED; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED TOMOGRAPHY; FACTUAL DATABASE; FEMALE; HUMAN; IMAGE PROCESSING; LUNG; MALE; MIDDLE AGED; PROCEDURES; RADIOGRAPHY; YOUNG ADULT;

ADULT; AGED; ALGORITHMS; DATABASES, FACTUAL; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; TOMOGRAPHY, X-RAY COMPUTED; YOUNG ADULT;

EID: 84914162074     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2337057     Document Type: Article

References (61)

1. Estimated prevalence and incidence of lung disease Am. Lung Assoc., Washington, DC, Tech. Rep., Apr. 2013.
2. M. S. Brown, M. F. Mcnitt-Gray, N. J. Mankovich, J. G. Goldin, J. Hiller, L. S. Wilson, and D. Aberie, "Method for segmenting chest CT image data using an anatomical model: Preliminary results," IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 828-839, Dec. 1997.
3. G. J. Kemerink, R. J. Lamers, B. J. Pellis, H. H. Kruize, and J. Van Engelshoven, "On segmentation of lung parenchyma in quantitative computed tomography of the lung," Med. Phys., vol. 25, p. 2432, 1998.
4. S.G.Armato, III and W. F. Sensakovic, "Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis," Acad. Radiol., vol. 11, no. 9, pp. 1011-1021, 2004.
5. I. Sluimer,M. Prokop, and B. Van Ginneken, "Toward automated segmentation of the pathological lung in CT," IEEE Trans. Med. Imag., vol. 24, no. 8, pp. 1025-1038, Aug. 2005.
6. U. Bagci, B. Foster, K. Miller-Jaster, B. Luna, B. Dey,W. R. Bishai, C. B. Jonsson, S. Jain, and D. J. Mollura, "A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging," Eur. J. Nucl.Med.Molecular Imag. Res., vol. 3, no. 55, pp. 1-20, 2013.
7. J.-M. Kuhnigk, H. Hahn, M. Hindennach, V. Dicken, S. Krass, and H.-O. Peitgen, "Lung lobe segmentation by anatomy-guided 3-D watershed transform," in Proc. Int. Soc. Opt. Photon. Med. Imag., 2003, pp. 1482-1490.
8. R. Shojaii, J. Alirezaie, and P. Babyn, "Automatic lung segmentation in CT images using watershed transform," in Proc. IEEE Int. Conf. Image Process., 2005, vol. 2, p. 270.
9. L. Zhang, E. A. Hoffman, and J. M. Reinhardt, "Lung lobe segmentation by graph search with 3-D shape constraints," in Proc. SPIE, 2001, vol. 4321, pp. 204-215.
10. P. Hua, Q. Song, M. Sonka, E. Hoffman, and J. Reinhardt, "Segmentation of pathological and diseased lung tissue in CT images using a graphsearch algorithm," in Proc. IEEE Int. Symp. Biomed. Imag: From Nano to Macro, 2011, pp. 2072-2075.
11. T. N. Jones and D. N. Metaxas, "Automated 3-D segmentation using deformable models and fuzzy affinity," in Information Processing in Medical Imaging.. New York: Springer, 1997, pp. 113-126.
12. Y. Zhou and J. Bai, "Multiple abdominal organ segmentation: An atlas-based fuzzy connectedness approach," IEEE Trans. Inf. Technol. Biomed., vol. 11, no. 3, pp. 348-352, May 2007.
13. T. F. Cootes, A. Hill, C. J. Taylor, and J. Haslam, "Use of active shape models for locating structures in medical images," Image Vis. Comput., vol. 12, no. 6, pp. 355-365, 1994.
14. T. Kitasaka, K. Mori, J.-I. Hasegawa, and J.-I. Toriwaki, "Lung area extraction from 3-D chest X-ray CT images using a shape model generated by a variable Bízier surface," Syst. Comput. Jpn., vol. 34, no. 4, pp. 60-71, 2003.
15. J. Pu, J. Roos, C.A.Yi, S. Napel, G.D. Rubin, and D. S. Paik, "Adaptive border marching algorithm: Automatic lung segmentation on chest CT images," Comput. Medi. Imag. Graph., vol. 32, no. 6, pp. 452-462, 2008.
16. U. Bagci, X. Chen, and J. K. Udupa, "Hierarchical scale-based multiobject recognition of 3-D anatomical structures," IEEE Trans. Med. Imag., vol. 31, no. 3, pp. 777-789, Mar. 2012.
17. X. Chen, J. K. Udupa, U. Bagci, Y. Zhuge, and J. Yao, "Medical image segmentation by combining graph cut and oriented active appearance models," IEEE Trans. Image Process., vol. 21, no. 4, pp. 2035-2046, Apr. 2012.
18. X. Chen and U. Bagci, "3-D automatic anatomy segmentation based on iterative graph-cut-ASM," Med. Phys., vol. 38, no. 8, pp. 4610-4622, 2011.
19. B.Li, G.E.Christensen, E. A. Hoffman,G.McLennan, and J. M. Reinhardt, "Establishing a normative atlas of the human lung: Intersubject warping and registration of volumetric CT images," Acad. Radiol., vol. 10, no. 3, pp. 255-265, 2003.
20. L. Zhang and J. Reinhardt, "3-D pulmonary CT image registration with a standard lung atlas," in Proc. SPIE Conf. Med. Imag., 2000, vol. 4322, pp. 67-77.
21. L. Zhang, E. A. Hoffman, and J. M. Reinhardt, "Atlas-driven lung lobe segmentation in volumetric X-ray CT images," IEEE Trans. Med. Imag., vol. 25, no. 1, pp. 1-16, Jan. 2006.
22. H. Park, P. H. Bland, and C. R. Meyer, "Construction of an abdominal probabilistic atlas and its application in segmentation," IEEE Trans. Med. Imag., vol. 22, no. 4, pp. 483-492, Apr. 2003.
23. T. Greitz, C. Bohm, S. Holte, and L. Eriksson, "A computerized brain atlas: Construction, anatomical content, and some applications," J. Comput. Assist. Tomogr., vol. 15, no. 1, pp. 26-38, 1991.
24. Y. Xu, M. Sonka, G. McLennan, J. Guo, and E. A. Hoffman, "MDCTbased 3-D texture classification of emphysema and early smoking related lung pathologies," IEEE Trans. Med. Imag., vol. 25, no. 4, pp. 464-475, Apr. 2006.
25. M. J. Gangeh, L. Sørensen, S. B. Shaker, M. S. Kamel, M. De Bruijne, and M. Loog, "A texton-based approach for the classification of lung parenchyma in CT images," in Medical Image Computing and Computer- Assisted Intervention-MICCAI 2010. New York: Springer, 2010, pp. 595-602.
26. J. Yao, A. Dwyer, R. M. Summers, and D. J. Mollura, "Computeraided diagnosis of pulmonary infections using texture analysis and support vector machine classification," Acad. Radiol., vol. 18, no. 3, pp. 306-314, 2011.
27. U. Bagci, J. Yao, A. Wu, J. Caban, T. N. Palmore, A. F. Suffredini, O. Aras, and D. J. Mollura, "Automatic detection and quantification of tree-in-bud (TIB) opacities from CT scans," IEEE Trans. Biomed. Eng., vol. 59, no. 6, pp. 1620-1632, Jun. 2012.
28. I. C. Sluimer, M. Prokop, I. Hartmann, and B. van Ginneken, "Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung," Med. Phys., vol. 33, p. 2610, 2006.
29. Y. Song,W. Cai, J. Kim, and D. D. Feng, "A multistage discriminative model for tumor and lymph node detection in thoracic images," IEEE Trans. Med. Imag., vol. 31, no. 5, pp. 1061-1075, May 2012.
30. X. Ye, X. Lin, G. Beddoe, and J. Dehmeshki, "Efficient computeraided detection of ground-glass opacity nodules in thoracic CT images," in Proc. 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2007, pp. 4449-4452.
31. L. Srensen, S. B. Shaker, and M. De Bruijne, "Quantitative analysis of pulmonary emphysema using local binary patterns," IEEE Trans. Med. Imag., vol. 29, no. 2, pp. 559-569, Feb. 2010.
32. J. K. Udupa and S. Samarasekera, "Fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation," Graph. Models Image Process., vol. 58, no. 3, pp. 246-261, 1996.
33. K. C. Ciesielski and J. K. Udupa, "Affinity functions in fuzzy connectedness based image segmentation i: Equivalence of affinities," Comput. Vis. Image Understand., vol. 114, no. 1, pp. 146-154, Jan. 2010.
34. K. C. Ciesielski and J. K. Udupa, "Affinity functions in fuzzy connectedness based image segmentation II: Defining and recognizing truly novel affinities," Comput. Vis. Image Understand., vol. 114, no. 1, pp. 155-166, Jan. 2010.
35. U. Baʇci, M. Bray, J. Caban, J. Yao, and D. J. Mollura, "Computerassisted detection of infectious lung diseases:A review," Comput.Med. Imag. Graph., vol. 36, no. 1, pp. 72-84, 2012.
36. D. M. Hansell, A. A. Bankier, H. MacMahon, T. C. McLoud, N. L. Müller, and J. Remy, "Fleischner society: Glossary of terms for thoracic imaging," Radiology, vol. 246, no. 3, pp. 697-722, 2008.
37. K.C.Ciesielski, J. K.Udupa, A. X. Falcão, and P.A.Miranda, "Fuzzy connectedness image segmentation in graph cut formulation: A lineartime algorithm and a comparative analysis," J. Math. Imag. Vis., vol. 44, no. 3, pp. 375-398, 2012.
38. S. Sun, C. Bauer, and R. Beichel, "Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach," IEEE Trans.Med. Imag., vol. 31, no. 2, pp. 449-460, Feb. 2012.
39. M.N.Prasad,M. S. Brown,S. Ahmad,F. Abtin, J. Allen, I. daCosta,H. J. Kim,M. F. McNitt-Gray, and J. G. Goldin, "Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs," Acad. Radiol., vol. 15, no. 9, pp. 1173-1180, 2008.
40. J. Wang, F. Li, and Q. Li, "Automated segmentation of lungs with severe interstitial lung disease in CT," Med. Phys., vol. 36, p. 4592, 2009.
41. M. Sofka, J. Wetzl, N. Birkbeck, J. Zhang, T. Kohlberger, J. Kaftan, J. Declerck, and S. Zhou, "Multi-stage learning for robust lung segmentation in challenging CT volumes," in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011, G. Fichtinger, A. Martel, and T. Peters, Eds. Berlin, Germany: Springer, 2011, vol. 6893, Lecture Notes Comput. Sci., pp. 667-674.
42. Z.Xu, U. Bagci, andD.Mollura, "Efficient ribcage segmentation from CT scans using shape features," in IEEE EMBC, Chicago, IL, 2014, pp. 2899-2902.
43. A. Frangi, W. Niessen, K. Vincken, and M. Viergever, "Multiscale vessel enhancement filtering," in Proc. MICCAI, 1998, vol. 1496, pp. 130-137.
44. J. A. Sethian, Level SetMethods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge, U.K.: Cambridge Univ. Press, 1999, vol. 3.
45. M.E.Visser,M.C.Stead,G.Walzl,R.Warren,M. Schomaker, H.M. S. Grewal, E. C. Swart, and G. Maartens, "Baseline predictors of sputum culture conversion in pulmonary tuberculosis: Importance of cavities, smoking, time to detection and w-Beijing genotype," PLoS One, vol. 7, no. 1, p. e29588, Jan. 2012.
46. Z. Xu, U. Bagci, A. Kubler, B. Luna, S. Jain, W. R. Bishai, and D. J. Mollura, "Computer-aided detection and quantification of cavitary tuberculosis from CT scans," Med. Phys., vol. 40, no. 11, 2013.
47. Y. Song, W. Cai, Y. Zhou, and D. Feng, "Feature-based image patch approximation for lung tissue classification," IEEE Trans. Med. Imag., vol. 32, no. 4, pp. 797-808, Apr. 2013.
48. Y. S. Park, J. B. Seo, N. Kim, E. J. Chae, Y. M. Oh, S. Do Lee, Y. Lee, and S.-H. Kang, "Texture-based quantification of pulmonary emphysema on high-resolution computed tomography: Comparison with density-based quantification and correlation with pulmonary function test," Invest. Radiol., vol. 43, no. 6, pp. 395-402, 2008.
49. Y. Abramowitz, N. Simanovsky, M. S. Goldstein, and N. Hiller, "Pleural effusion: Characterization with CT attenuation values and CT appearance," Am. J. Roentgenol., vol. 192, no. 3, pp. 618-623, 2009.
50. K. R. Nandalur, A. H. Hardie, S. R. Bollampally, J. P. Parmar, and K. D. Hagspiel, "Accuracy of computed tomography attenuation values in the characterization of pleural fluid: An ROC study," Acad. Radiol., vol. 12, no. 8, pp. 987-991, 2005.
51. J. Yao, J. Bliton, and R. Summers, "Automatic segmentation and measurement of pleural effusions on CT," IEEE Trans. Biomed. Eng., vol. 60, no. 7, pp. 1834-1840, Jul. 2013.
52. J. K. Leader, R.M. Rogers, C. R. Fuhrman, F. C. Sciurba, B. Zheng, P. F. Thompson, J. L.Weissfeld, S. K. Golla, and D. Gur, "Size and morphology of the trachea before and after lung volume reduction surgery," Am. J. Roentgenol., vol. 183, no. 2, pp. 315-321, 2004.
53. Z.Xu, U. Bagci, andD.Mollura, "Accurate and efficient separation of left and right lungs from 3-D CT scans: A generic hysterisis aproach," in IEEE EMBC, Chicago, IL, 2014, pp. 6036-6039.
54. M. Sonka et al., Image Processing, Analysis, and Machine Vision. Pacific Grove, CA: PWS, 1999.
55. J.-M. Kuhnigk et al., "New tools for computer assistance in thoracic CT. Part 1. Functional analysis of lungs, lung lobes, and bronchopulmonary segments," Radiographics, vol. 25, no. 2, pp. 525-536, 2005.
56. O. Weinheimer, T. Achenbach, C. P. Heussel, and C. Düber, "Automatic lung segmentation inMDCT images," in Proc. 4th Int.Workshop Pulmonary Image Anal., 2011, pp. 241-255.
57. F. Malmberg, R. Strand, J. Kullberg, R. Nordenskjöld, and E. Bengtsson, "Smart paint-A new interactive segmentation method applied to MR prostate segmentation," in MICCAI 2012 Workshop Prostate MR Image Segmentation Grand Challenge (PROMISE'12) , Nice, France, 2012, p. 1.
58. E. van Rikxoort and B. van Ginneken, "Automatic segmentation of the lungs and lobes from thoracic CT scans," in Proc. 4th Int. Workshop Pulmonary Image Anal., 2011, pp. 261-268.
59. A. Montillo, "Context selective decision forests and their application to lung segmentation in CT images," in Proc. MICCAI Workshop Pulmonary Image Anal., Toronto, Canada, 2011, p. 1.
60. S. Sun, C. Bauer, and R. Beichel, "Robust active shape model based lung segmentation in CT scans," in Proc. 4th Int.Workshop Pulmonary Image Anal., 2011, pp. 213-224.
61. A. Mansoor, U. Bagci, and D. Mollura, "Near-optimal keypoint sampling for fast pathological lung segmentation," in Proc. IEEE EMBC, Chicago, IL, 2014, pp. 6032-6035.