Lung lesion extraction using a toboggan based growing automatic segmentation approach

IEEE Transactions on Medical Imaging

Volumn 35, Issue 1, 2016, Pages 337-353

  Song, Jiangdian ^a,b   Yang, Caiyun ^b   Fan, Li ^c   Wang, Kun ^b   Yang, Feng ^d   Liu, Shiyuan ^c   Tian, Jie ^b  

^a NORTHEASTERN UNIVERSITY   (China)

^b INSTITUTE OF AUTOMATION   (China)

^c SECOND MILITARY MEDICAL UNIVERSITY   (China)

^d BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Back off mechanism; Computed tomography (CT); Lung lesion segmentation; Region growing; Toboggan

Indexed keywords

BIOLOGICAL ORGANS; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; EXTRACTION; GRAPHIC METHODS; NUMERICAL METHODS; SPORTING GOODS; TOMOGRAPHY;

AUTOMATIC SEGMENTATIONS; BACK-OFF MECHANISMS; COMPUTED TOMOGRAPHY SCAN; LESION SEGMENTATIONS; LUNG LESIONS; REGION GROWING; SEGMENTATION ACCURACY; TOBOGGAN;

IMAGE SEGMENTATION;

ALGORITHM; DIAGNOSTIC IMAGING; FACTUAL DATABASE; HUMAN; IMAGE PROCESSING; LUNG TUMOR; PATHOLOGY; PROCEDURES; X-RAY COMPUTED TOMOGRAPHY;

ALGORITHMS; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG NEOPLASMS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84959354566     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2474119     Document Type: Article

References (45)

1. R. Siegel, D. Naishadham, and A. Jemal, "Cancer statistics, 2013," CA Cancer J Clin, vol. 63, pp. 11-30, Jan. 2013.
2. W. H. Organization, "Description of the global burden of NCDs, their risk factors and determinants," Geneva, Switzerland: World Health Organization, 2011.
3. H. J. W. L. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Cavalho, J. Bussink, R. Monshouwer, B. Haibe-Kains, D. Rietveld, F. Hoebers, M. M. Rietbergen, C. R. Leemans, A. Dekker, J. Quackenbush, R. J. Gillies, and P. Lambin, "Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.," Nat. Commun., vol. 5, p. 4006, 2014.
4. J. Song and C. Yang et al., "A New Quantitative Radiomics Approach for Non-Small Cell Lung Cancer (NSCLC) Prognosis," in presented at the 101nd Int. Conf. Radiological Society of North America, Chicago, Illinois, November 29-December 04 2015.
5. M. Nakata, H. Saeki, I. Takata, Y. Segawa, H. Mogami, K. Mandai, and K. Eguchi, "Focal ground-glass opacity detected by low-dose helical CT," Chest, vol. 121, no. 5, pp. 1464-1467, 2002.
6. S. Candemir, S. Jaeger, K. Palaniappan, J. P. Musco, R. K. Singh, Z. Xue, A. Karargyris, S. Antani, G. Thoma, and C. J. McDonald, "Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration," IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 577-590, 2014.
7. Bian and Zijian et al., "Accurate airway centerline extraction based on topological thinning using graph-Theoretic analysis," Bio-medical materials and engineering, vol. 24, no. 6, pp. 3239-3249, 2014.
8. B. Lassen, E. M. Van Rikxoort, M. Schmidt, S. Kerkstra, B. Van Ginneken, and J. M. Kuhnigk, "Automatic segmentation of the pulmonary lobes from chest CT scans based on Fissures, Vessels, Bronchi," IEEE Trans. Med. Imaging, vol. 32, no. 2, pp. 210-222, 2013.
9. D. M. Campos, A. Simões, I. Ramos, and A. Campilho, "Feature-Based Supervised Lung Nodule Segmentation," no. Ci, pp. 23-26, 2014.
10. S. Diciotti, G. Picozzi, M. Falchini, M. Mascalchi, N. Villari, and G. Valli, "3-D segmentation algorithm of small lung nodules in spiral CT images.," IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 1, pp. 7-19, 2008.
11. S. G. Armato, G. McLennan, D. Hawkins, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P. Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. Kazerooni, H. MacMahon, E. J. R. Van Beeke, D. Yankelevitz, A. M. Biancardi, P. H. Bland, M. S. Brown, R. M. Engelmann, G. E. Laderach, D. Max, R. C. Pais, D. P. Y. Qing, R. Y. Roberts, A. R. Smith, A. Starkey, P. Batrah, P. Caligiuri, A. Farooqi, G. W. Gladish, C. M. Jude, R. F. Munden, I. Petkovska, L. E. Quint, L. H. Schwartz, B. Sundaram, L. E. Dodd, C. Fenimore, D. Gur, N. Petrick, J. Freymann, J. Kirby, B. Hughes, A. Vande Casteele, S. Gupte, M. Sallamm, M. D. Heath, M. H. Kuhn, E. Dharaiya, R. Burns, D. S. Fryd, M. Salganicoff, V. Anand, U. Shreter, S. Vastagh, B. Y. Croft, and L. P. Clarke, "The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.," Med. Phys., vol. 38, no. 2, pp. 915-931, 2011.
12. S. Diciotti, S. Lombardo, M. Falchini, G. Picozzi, and M. Mascalchi, "Automated segmentation refinement of small lung nodules in CT scans by local shape analysis," IEEE Trans. Biomed. Eng., vol. 58, no. 12, pt. 1, pp. 3418-3428, 2011.
13. D. Wu, L. Lu, J. Bi, Y. Shinagawa, K. Boyer, A. Krishnan, and M. Salganicoff, "Stratified learning of local anatomical context for lung nodules in CT images," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 2791-2798.
14. A. a Farag, H. E. A. El Munim, J. H. Graham, and A. a Farag, "A novel approach for lung nodules segmentation in chest CT using level sets.," IEEE Trans. Image Process., vol. 22, no. 12, pp. 5202-5213, 2013.
15. S. Sun, Y. Guo, Y. Guan, and H. Ren, "Juxta-Vascular Nodule Segmentation Based on the Flowing Entropy and Geodesic Distance Feature," Scientia Sinica(Informationis), vol. 61, pp. 1136-1146, 2013.
16. Y. Gu, V. Kumar, L. O. Hall, D. B. Goldgof, C. Y. Li, R. Korn, C. Bendtsen, E. R. Velazquez, A. Dekker, H. Aerts, P. Lambin, X. Li, J. Tian, R. A. Gatenby, and R. J. Gillies, "Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach," Pattern Recognit., vol. 46, no. 3, pp. 692-702, 2013.
17. C. Bendtsen, M. Kietzmann, R. Korn, P. D. Mozley, G. Schmidt, and G. Binnig, "X-Ray computed tomography: Semiautomated volumetric analysis of late-stage lung tumors as a basis for response assessments," Int. J. Biomed. Imaging, vol. 2011, 2011.
18. M. Athelogou, G. Schmidt, A. Schäpe, M. Baatz, and G. Binnig, "Cognition Network Technology -A Novel Multimodal Image Analysis Technique for Automatic Identification and Quantification of Biological Image Contents," Imaging Cellular and Molecular Biological Functions, pp. 407-422, 2007.
19. Q. Wang, E. Song, R. Jin, P. Han, X. Wang, Y. Zhou, and J. Zeng, "Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.," Acad. Radiol., vol. 16, no. 6, pp. 678-688, 2009.
20. T. Kubota, A. K. Jerebko, M. Dewan, M. Salganicoff, and A. Krishnan, "Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models," Med. Image Anal., vol. 15, no. 1, pp. 133-154, 2011.
21. A. Mansoor, U. Bagci, Z. Xu, B. Foster, K. N. Olivier, and J. M. Elinoff et al., "A generic approach to pathological lung segmentation," IEEE Trans Med Imaging, vol. 33, pp. 2293-2310, Dec. 2014.
22. S. Hu, E. a. Hoffman, and J. M. Reinhardt, "Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images," IEEE Trans. Med. Imaging, vol. 20, no. 6, pp. 490-498, 2001.
23. A. S. 3Rd and S. WF, "Automated lung segmentation for thoracic CT impact on computer-aided diagnosis," Academic Radiology, vol. 11, pp. 1011-1021, 2004.
24. M. Kakar and D. R. Olsen, "Automatic segmentation and recognition of lungs and lesion from CT scans of thorax," Comput. Med. Imaging Graph., vol. 33, no. 1, pp. 72-82, 2009.
25. S. G. Armato, M. L. Giger, and H. MacMahon, "Automated detection of lung nodules in CT scans: Preliminary results.," Med. Phys., vol. 28, no. 8, pp. 1552-1561, 2001.
26. A. Madabhushi and D. N. Metaxas, "Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions," IEEE Trans.Med. Imaging, vol. 22, no. 2, pp. 155-169, 2003.
27. K. Murphy, B. Van Ginneken, A. M. R. Schilham, B. J. De Hoop, H. a. Gietema, and M. Prokop, "A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification," Med. Image Anal., vol. 13, no. 5, pp. 757-770, 2009.
28. T. Messay, R. C. Hardie, and S. K. Rogers, "A new computationally efficient CAD system for pulmonary nodule detection in CT imagery," Med. Image Anal., vol. 14, no. 3, pp. 390-406, 2010.
29. B. Golosio, G. L. Masala, A. Piccioli, P. Oliva, M. Carpinelli, R. Cataldo, P. Cerello, F. De Carlo, F. Falaschi, M. E. Fantacci, G. Gargano, P. Kasae, and M. Torsello, "A novel multithreshold method for nodule detection in lung CT.," Med. Phys., vol. 36, no. 8, pp. 3607-3618, 2009.
30. M. Tan, R. Deklerck, B. Jansen, M. Bister, and J. Cornelis, "A novel computer-aided lung nodule detection system for CT images," Med. Phys., vol. 38, no. 10, p. 5630, 2011.
31. J. Fairfield, "Toboggan contrast enhancement for contrast segmentation," in [1990] Proceedings. 10th Int. Conf. Pattern Recognit., 1990, vol. i, pp. 712-716.
32. E. N. Mortensen and W. a. Barrett, "Toboggan-based intelligent scissors with a four-parameter edge model," in Proceedings. 1999 IEEE Comput. Soc. Conf.Comput. Vis. Pattern Recognit. (Cat. No PR00149), 1999, vol. 2, pp. 452-458.
33. H. Ibrahim, M. Petrou, K. Wells, S. Doran, and O. Olsen, "Preprocessing for use in automatic volumetric liver segmentation from NMR data," in IEEE Symp. Conf. Rec. Nucl. Sci. 2004, 2004, vol. 5, no. C, pp. 2783-2787.
34. Y. C. Lin, Y. P. Tsai, Y. P. Hung, and Z. C. Shih, "Comparison between immersion-based and toboggan-based watershed image segmentation," IEEE Trans. Image Process., vol. 15, no. 3, pp. 632-640, 2006.
35. C. J. Armstrong, B. L. Price, and W. a. Barrett, "Interactive segmentation of image volumes with Live Surface," Comput. Graph., vol. 31, no. 2, pp. 212-229, 2007.
36. W. Tan, J. Yang, Z. Bian, Z. Gong, and D. Zhao, "Automatic extraction of 3d airway tree from multislice computed tomography images," Journal of Medical Imaging & Health Informatics, vol. 4, no. 8, pp. 768-775, 2014.
37. N. Baka, C. T. Metz, C. J. Schultz, R. J. Van Geuns, W. J. Niessen, and T. Van Walsum, "Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration," IEEE Trans. Med. Imaging, vol. 33, no. 5, pp. 1023-1034, 2014.
38. W. K. Moon, Y.-W. Shen, M. S. Bae, C.-S. Huang, J.-H. Chen, and R.-F. Chang, "Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound images.," IEEE Trans. Med. Imaging, vol. 32, no. 7, pp. 1191-1200, 2013.
39. D. S. Paik, C. F. Beaulieu, G. D. Rubin, B. Acar, R. B. Jeffrey, J. Yee, J. Dey, and S. Napel, "Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT," IEEE Trans. Med. Imaging, vol. 23, no. 6, pp. 661-675, 2004.
40. H. Wang, D. B. Stout, and A. F. Chatziioannou, "Estimation of mouse organ locations through registration of a statistical mouse atlas with micro-CT images," IEEE Trans. Med. Imaging, vol. 31, no. 1, pp. 88-102, 2012.
41. M. Rajchl, J. Yuan, J. White, E. Ukwatta, J. Stirrat, C. Nambakhsh, F. Li, and T. Peters, "Interactive Hierarchical Max-Flow Segmentation of Scar Tissue from Late-Enhancement Cardiac MR Images.," IEEE Trans. Med. Imaging, vol. 33, no. 1, pp. 159-172, 2013.
42. D. Mahapatra, P. J. Schüf, J. a. W. Tielbeek, J. C. Makanyanga, J. Stoker, S. a Taylor, F. M. Vos, and J. M. Buhmann, "Automatic Detection and Segmentation of Crohn ' s Disease Tissues From Abdominal MRI," IEEE Trans. Med. Imaging, vol. 32, no. 12, pp. 2332-2347, 2013.
43. C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, "A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2007-2016, 2011.
44. D. Xiang, J. Tian, F. Yang, Q. Yang, X. Zhang, Q. Li, and X. Liu, "Skeleton Cuts-An Efficient Segmentation Method for Volume Rendering," IEEE Trans. Vis. Comput. Graph., vol. 17, no. 9, pp. 1295-1306, 2011.
45. J. Tian, J. Xue, Y. Dai, J. Chen, and J. Zheng, "A novel software platform for medical image processing and analyzing," IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 6, pp. 800-812, 2008.