Skip to main content
SpringerLink
Log in

Automated detection of lung nodules in computed tomography images: a review

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Austin J.H., Mueller N.L., Friedman P.J.: Glossary of terms for CT of the lungs: recommendations of the nomenclature. Comm. Fleischner Soc. Radiol. 200, 327–331 (1996)

    Google Scholar 

  2. Kostis W.J., Reeves A.P., Yankelevitz D.F. et al.: Three- dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans. Medical Imaging 22, 1259–1274 (2003)

    Article  Google Scholar 

  3. Diciotti S., Picozzi G., Falchini M. et al.: 3D segmentation algorithm of small lung nodules in spiral CT images. IEEE Trans. Inf. Technol. Biomedical 12, 7–19 (2008)

    Article  Google Scholar 

  4. Li Q.: Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput. Med. Imaging Graph. 31, 248–257 (2007)

    Article  Google Scholar 

  5. Sluimer I.C., Schilham A., Prokop M. et al.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Medical Imaging 25, 385–405 (2006)

    Article  Google Scholar 

  6. Jeong, Y.J., Yi, C.A., Lee, K.S.: Solitary pulmonary nodules: detection, characterization, and guidance for further diagnostic workup and treatment. AJR 188 (2007)

  7. Parrish F.J.: Volume CT: state-of-the-art reporting. AJR 189, 528–534 (2007)

    Article  Google Scholar 

  8. Doi K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31, 198–211 (2007)

    Article  Google Scholar 

  9. Marten K., Engelke C.: Computer-aided detection and automated CT volumetry of pulmonary nodules. Eur Radiol 17, 888–901 (2007)

    Article  Google Scholar 

  10. ELCAP public lung image database. Vision and Image Analysis Group (VIA) and International Early Lung Cancer Action Program (I-ELCAP) Labs, Cornell University. http://www.via.cornell.edu/lungdb.html (2007)

  11. Public lung database to address drug response. Vision and Image Analysis Group (VIA) and International Early Lung Cancer Action Program (I-ELCAP) Labs, Cornell University. http://www.via.cornell.edu/crpf.html (2007)

  12. Lung Imaging Database Consortium (LIDC). http://imaging.cancer.gov/programsandresources/InformationSystems/LIDC (2007)

  13. Medical image database. MedPix. http://rad.usuhs.edu/medpix/index.html (2007)

  14. Wang J., Engelmann R., Li Q.: Segmentation of pulmonary nodules in three-dimensional CT images by use of a spiral-scanning technique. Med. Phys. 34, 4678–4689 (2007)

    Article  Google Scholar 

  15. Ochs R.A., Goldin J.G., Fereidoun A. et al.: Automated classification of lung bronchovascular anatomy in CT using Adaboost. Med. Image Anal. 11, 315–324 (2007)

    Article  Google Scholar 

  16. Ozekes S., Camurcu A.Y.: Automatic lung nodule detection using template matching. In: Yakhno T. E. N. (ed.) Lecture Notes in Computer Science, vol. 4243. pp. 247–253 (2006)

  17. Osman O., Ozekes S., Ucan O.N.: Lung nodule diagnosis using 3D template matching. Comput. Biol. Med. 37, 1167–1172 (2007)

    Article  Google Scholar 

  18. Ozekes S., Osman O., Ucan O.N.: Nodule detection in a lung region that’s segmented with using genetic cellular neural networks and 3D template matching with fuzzy rule based thresholding. Korean J. Radiol. 9, 1–9 (2008)

    Article  Google Scholar 

  19. Korfiatis, P., Kalogeropoulou, C., Costaridou, L.: Computer aided detection of lung nodules in multislice computed tomography. In: Proceedings of the International Special Topic Conference on Information Technology in Biomedicine (IEEE-ITAB 2006), p. 4. Ioannina, Epirus, Greece (2006)

  20. Garnavi, R., Baraani-Dastjerdi, A., Abrishami Moghaddam, H., et al.: A new segmentation method for lung HRCT images. In: Lovell, B.C., Maeder, A.J., Caelli, T., Ourselin, S. (eds.): Proceedings of the Digital Imaging Computing: Techniques and Applications, p. 8. IEEE CS Press, Cairns Convention Centre, Brisbane, Australia (2005)

  21. Kim, H., Nakashima, T., Itai, Y., et al.: Automatic detection of ground glass opacity from the thoracic MDCT images by using density features. In: International Conference on Control, Automation and Systems, pp. 1274–1277. IEEE Xplore, COEX, Seoul, Korea (2007)

  22. Gurcan M.N., Sahiner B., Petrick N. et al.: Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29, 2552–2558 (2002)

    Article  Google Scholar 

  23. Lin, D.T., Yan, C.R.: Lung nodules identification rules extraction with neural fuzzy network. In: Wang, L., Rajapakse, J.C., Fukushima, K., Lee, S.Y., Yao, X. (eds.): 9th International Conference of Information Processing (ICONIP), IEEE, Singapore (2002)

  24. Lin D.T., Yan C.R., Chen W.T.: Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system. Comput. Med. Imaging Graph. 29, 447–458 (2005)

    Article  Google Scholar 

  25. Pu J., Roos J., Yi C.A. et al.: Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Comput. Med. Imaging Graph. 32, 452–462 (2008)

    Article  Google Scholar 

  26. Wei, G.Q., Fan, L., Qian, J.: Automatic detection of nodules attached to vessels in lung CT by volume projection analysis. Medical Image Computing and Computer-assisted Intervention—MICCAI, vol. 2488, pp. 746–752. Springer, Berlin (2002)

  27. Gori, I., Bellotti, R., Cerello, P., et al.: Lung nodule detection in screening computed tomography. IEEE Nuclear Science Symposium Conference Record. IEEE (2006)

  28. Retico A., Delogu P., Fantacci M.E. et al.: Lung nodule detection in low-dose and thin-slice computed tomography. Comput. Biol. Med. 38, 525–534 (2008)

    Article  Google Scholar 

  29. Kawata Y., Niki N., Ohmatsu H. et al.: Quantitative surface characterization of pulmonary nodules based on thin-section CT images. IEEE Trans. Nuclear Sci. 45, 2132–2138 (1998)

    Article  Google Scholar 

  30. Arimura H., Katsuragawa S., Suzuki K. et al.: Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad. Radiol. 11, 617–629 (2004)

    Article  Google Scholar 

  31. Kubo, M., Kubota, K., Yamada, N., et al.: A CAD system for lung cancer based on low dose single-slice CT image. In: Proc. SPIE, vol. 4684, pp. 1672–1680 (2002)

  32. Sluimer I.C., van Waes P.F., Vierever M.A. et al.: Computer-aided diagnosis in high resolution CT of the lungs. Med. Phys. 30, 3081–3090 (2003)

    Article  Google Scholar 

  33. Bae K.T., Kim J.S., Na Y.H. et al.: Pulmonary nodules: automated detection on CT images with morphologic matching algorithm—preliminary results. Radiology 236, 286–294 (2005)

    Article  Google Scholar 

  34. Paik D.S., Beaulieu C.F., Rubin G.D. et al.: Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans. Med. Imaging 23, 661–675 (2004)

    Article  Google Scholar 

  35. Oda, T., Kubo, M., Kawata, Y., et al.: A detection algorithm of lung cancer candidate nodules on multi-slice CT images. In: Proc. of SPIE, vol. 4684. pp. 1354–1361 (2002)

  36. Farag A., El-Baz A., Gimel’farb G.G. et al.: Automatic detection and recognition of lung abnormalities in helical CT images using deformable templates. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical image Computing and Computer- assisted Intervention—MICCAI, vol. 3217, pp. 856–864. Springer, Berlin (2004)

    Google Scholar 

  37. Farag, A.A., El-Baz, A., Gimel’farb, G., et al.: Detection and recognition of lung abnormalities using deformable templates. In: Proceeding of 17th International Conference on Pattern Recognition (CVIP), pp. 23–26, August 2004. IEEE (2004)

  38. Zhao, J., Chang, S., Metaxas, D.N., et al.: An automatic method for ground glass opacity nodule detection and segmentation from CT studies. In: Proceedings of 28th IEEE EMBS Conference, pp. 3062–3065. IEEE Xplore, New York, USA (2006)

  39. Chang, S., Emoto, H., Metaxas, D.N., et al.: Pulmonary micronodule detection from 3-d chest CT. Medical image computing and computer-assisted intervention, vol. 3217, pp. 821–828. Springer, Berlin (2004)

  40. Matsumoto S., Kundel H.L., Gee J.C. et al.: Pulmonary nodule detection in CT images with quantized convergence index filter. Medi. Image Anal. 10, 343–352 (2006)

    Article  Google Scholar 

  41. Ezoe, T., Takizawa, H., Yamamoto, S., et al.: An automatic detection method of lung cancers including ground glass opacities from chest X-ray CT images. In: Proc. of SPIE, vol. 4684, pp. 1672–1680 (2002)

  42. Tanino, M., Takizawa, H., Yamamoto, S., et al.: A detection method of ground glass opacities in chest X-ray CT images using automatic clustering techniques. In: Proc. of SPIE, vol. 5032, pp. 1728–1737 (2003)

  43. Awai K., Murao K., Ozawa A. et al.: Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists’ detection performance. Radiology 230, 347–352 (2004)

    Article  Google Scholar 

  44. Fukano, G., Takizawa, H., Shigemoto, K., et al.: Recognition method of lung nodules using blood vessel extraction techniques and 3-D object models. In: Proc. of SPIE, vol. 5032, pp. 190–196 (2003)

  45. Li, Q., Doi, K.: New selective enhancement filter and its application for significant improvement of nodule detection on computed tomography. In: Proc. of SPIE, vol. 5370, pp. 1–9 (2004)

  46. Li Q., Li F., Doi K.: Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad. Radiol. 15, 165–175 (2008)

    Article  MATH  Google Scholar 

  47. Yu, Y., Zhao, H.: Enhancement filter for computer-aided detection of pulmonary nodules on thoracic CT images. In: Proceeding of the Sixth International Conference on Intelligent Systems Design and Applications. IEEE Xplore (2006)

  48. Fetita, C.I., Prêteux, F.J., Beigelman-Aubry, C., et al.: 3-D automated lung nodule segmentation in HRCT. Lecture Notes in Computer Science, vol. 2878/2003, pp. 626–634. Springer, Berlin (2003)

  49. Sun, S.-S., Li, H., Hou, X.-R., et al.: Automatic segmentation of pulmonary nodules in CT images. In: 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 790–793. IEEE (2007)

  50. Jia, T., Zhao, D.-Z., Yang, J.-Z., et al.: Automated detection of pulmonary nodules in HRCT images. In: 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 833–836. IEEE (2007)

  51. Xu, N., Ahuja, N., Bansal, R.: Automated lung nodule segmentation using dynamic programming and em-based classification. In: Sonka, M., Fitzpatrick, J.M. (eds.) Proc. of SPIE, vol. 4684, pp. 666–676 (2002)

  52. Aoyama M., Li Q., Katsuragawa S. et al.: Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images. Med. Phys. 30, 387–394 (2003)

    Article  Google Scholar 

  53. Kawata, Y., Niki, N., Ohmatsu, H., et al.: Hybrid classification approach of malignant and benign pulmonary nodules based on topological and histogram features. Medical Image Computing and Computer-assisted Intervention—MICCAI 2000, vol. 1935/2000, pp. 297–306. LNCS Springer Berlin/Heidelberg, Berlin (2000)

  54. Kim D.Y., Kim J.H., Noh S.M. et al.: Pulmonary nodule detection using chest CT images. Acta. Radiol. 44, 252–257 (2003)

    Google Scholar 

  55. Bellotti R., De Carlo F., Gargano G. et al.: A CAD system for nodule detection in low-dose lung cts based region growing and active contour models. Med. Phys. 34, 4901–4910 (2007)

    Article  Google Scholar 

  56. Itai Y., Hyoungseop K., Ishida T. et al.: A segmentation method of lung areas by using snakes and automatic detection of abnormal shadow on the areas. Int. J. Innov. Comput. Inf. Control 3, 277–284 (2007)

    Google Scholar 

  57. Zhao B., Yankelevitz D., Reeves A. et al.: Twodimensional multi-criterion segmentation of pulmonary nodules on helical CT images. Med. Phys. 26, 889–895 (1999)

    Article  Google Scholar 

  58. Ko, J.P., Rusinek, H., Jacobs, E., et al.: Volume quantitation of small pulmonary nodules on low dose chest CT: a phantom study. In: Radiological Society of North America 87th Scientific Assembly and Annual Meeting, Chicago (2001)

  59. Okada K., Comaniciu D., Krishnan A.: Robust anisotropic Gaussian fitting for columetric characterization of pulmonary nodules in multislice ct. IEEE Trans. Med. Imaging 24, 409–423 (2005)

    Article  Google Scholar 

  60. Fan, L., Qian, J., Odry, B.L., et al.: Automatic segmentation of pulmonary nodules by using dynamic 3-d cross-correlation for interactive CAD systems. In: Proc. SPIE Med. Imag, vol. 4684, pp. 1362–1369 (2002)

  61. Enquobahrie, A.A., Reeves, A.P., Yankelevitz, D.F., et al.: Automated detection of pulmonary nodules from whole lung helical CT scans: performance comparison for isolated and attached nodules. In: Proc. of SPIE, vol. 5370. pp. 791–800, columetric (2004)

  62. Kuhnigk J.-M., Dicken V., Bornemann L. et al.: Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans. Med. Imaging 25, 417–434 (2006)

    Article  Google Scholar 

  63. Kanazawa K., Kawata Y., Niki Y. et al.: Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput. Med. Imaging Graph. 22, 157–167 (1998)

    Article  Google Scholar 

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

    Article  Google Scholar 

  65. Lee, Y., Tsai, D.Y., Hara, T., et al.: Improvement in automated detection of pulmonary nodules on helical X-ray CT images. In: Proc. of SPIE, vol. 5370, pp. 824–832. (2004)

  66. McNitt-Gary M.F., Hart E., Wyckoff M.N. et al.: A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med. Phys. 26, 880–888 (1999)

    Article  Google Scholar 

  67. Shah S.K., McNitt-Gray M.F., Rogers S.R. et al.: Computer aided characterization of solitary pulmonary nodules using volumetric and contrast enhancement features. Acad. Radiol. 12, 1310–1319 (2005)

    Article  Google Scholar 

  68. Udupa J.K., Saha P.K., Lotufo R.A.: Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1485–1500 (2002)

    Article  Google Scholar 

  69. Dehmeshki J., Amin H., Valdivieso M. et al.: Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans. Medi. Imaging 27, 467–480 (2008)

    Article  Google Scholar 

  70. Klik, M.A.J., Rikxoort, E.M.v., Peters, J.F., et al.: Improved classification of pulmonary nodules by automated detection of benign subpleural lymph nodes. ISBI (2006)

  71. El-Baz, A., Gimel’farb, G., Falk, R., et al.: A new CAD system for early diagnosis of detected lung nodules. IEEE Int. Conf. Image Process. 2:II-461–II-464 (2007)

    Google Scholar 

  72. Giger M.L., Bae K.T., MacMahon H.: Computerized detection of pulmonary nodules in computed tomography images. Invest. Radiol. 29, 459–465 (1994)

    Article  Google Scholar 

  73. Wang P., DeNuzio A., Okunieff P. et al.: Lung metastases detection in CT images using 3D template matching. Med. Phys. 34, 915–922 (2007)

    Article  Google Scholar 

  74. Antonelli, M., Frosini, G., Lazzerini, B., et al.: A CAD system for lung nodule detection based on an anatomical model and a fuzzy neural network. North American Fuzzy Information Processing Society, vol. pp. 448–453. IEEE Xplore (2006)

  75. Okada, K., Comaniciu, D., Krishnan, A.: Robust 3D segmentation of pulmonary nodules in multislice CT images. Lecture Notes in Computer Science, pp. 881–889. Springer, Berlin (2004)

  76. Takizawa H., Yamamoto S., Shiina T.: Accuracy improvement of pulmonary nodule detection based on spatial statistical analysis of thoracic CT scans. IEICE Trans. Inf. Syst. 90-D, 1168–1174 (2007)

    Article  Google Scholar 

  77. Armato S.G., Giger M.L., Moran C.J. et al.: Computerized detection of pulmonary nodules on CT scans. Radiographics 19, 1303–1311 (1999)

    Google Scholar 

  78. Zhang, X., McLennan, G., Hoffman, E.A., et al.: Computerized detection of pulmonary nodules using cellular neural networks in CT images. In: Proc. of SPIE, vol. 5370, pp. 30–41 (2004)

  79. Ginneken, B.v.: Supervised probabilistic segmentation of pulmonary nodules in CT scans. In: 9th Medical Image Computing and Computer-assisted Intervention –MICCAI Conference. Springer, Berlin (2006)

  80. Dehmeshki, J., Chen, J., Casique, M.V., et al.: Classification of lung data by sampling and support vector machine. 26th In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS ‘04), vol. 2, pp. 3194–3197 (2004)

  81. Zhao L., Boroczky L., Lee K.P.: False positive reduction for lung nodule CAD using support vector machines and genetic algorithms. Int. Congress Ser. 1281, 1109–1114 (2005)

    Article  Google Scholar 

  82. Suzuki K., Li F., Sone S. et al.: Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network. IEEE Trans. Med. Imaging 24, 1138–1150 (2005)

    Article  Google Scholar 

  83. Suzuki K., Li F., Li Q. et al.: Comparison between 2D and 3D massive-training ANNs (MTANNs) in CAD for lung nodule detection on MDCT. Int. J. Comput. Assist. Radiol. Surg. 1, 354–355 (2006)

    Google Scholar 

  84. Suzuki K.: A supervised ‘lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys. Med. Biol. 54, S31–S45 (2005)

    Article  Google Scholar 

  85. Brown M.S., McNitt-Gray M.F., Mankovich N.J. et al.: Method for segmenting chest CT image data using an anatomical model: preliminary results. IEEE Trans. Med. Imaging 16, 828–839 (1997)

    Article  Google Scholar 

  86. Lin J.-S., Lo S.C.B., Hasegawa A. et al.: Reduction of false positives in lung nodule detection using a two-level neural classification. IEEE Trans. Med. Imaging 15, 206–216 (1996)

    Article  Google Scholar 

  87. Pereira C.S., Alexandre L.A., Mendonça A.M. et al.: A multiclassifier approach for lung nodule classification. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition, vol. 4142/2006, pp. 612–623. Springer Berlin/Heidelberg, Berlin (2006)

    Chapter  Google Scholar 

  88. Boroczky L., Zhao L., Lee K.P.: Feature subset selection for improving the performance of false positive reduction in lung nodule CAD. IEEE Trans. Inf. Technol. Biomed. 10, 504–511 (2006)

    Article  Google Scholar 

  89. McCulloch C.C., Kaucic R.A., Mendonca P.R. et al.: Model-based detection of lung nodules in computed tomography exams. Thoracic computer-aided diagnosis. Acad. Radiol. 11, 258–266 (2004)

    Article  Google Scholar 

  90. Lee, Y., Hara, T., Fujita, H., et al.: Nodule detection on chest helical CT scans by using a genetic algorithm. In: Proceedings of Intelligent Information Systems, IIS ‘97., pp. 67–70. Grand Bahama Island, Bahamas (1997)

  91. Dehmeshki, J., Ye, X., Casique, M.V., et al.: A hybrid approach for automated detection of lung nodules in CT images. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp 506–509. IEEE (2006)

  92. Kouzani, A., Lee, S., Hu, E.J.: Lung nodules detection by ensemble classification. In: Poo, A.-N. (ed.) Proceedings of 2008 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2008), pp. 324-329. IEEE Xplore, Singapore (2008)

  93. Lee, S.L.A., Kouzani, A.Z., Hu, E.J.: Random forest based lung nodule classification aided by clustering. Comput. Med. Imaging Graph. (2010) (in press)

  94. Armato S.G., Li F., Giger M.L. et al.: Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 225, 685–692 (2002)

    Article  Google Scholar 

  95. Ge, Z., Sahiner, B., Chan, H.P., et al.: Computer aided detection of lung nodules: False positive reduction using a 3-d gradient field method. Proc. of SPIE, vol. 5370. pp. 1076-1082. (2004)

  96. Saita, S., Oda, T., Kubo, M., et al.: Nodule detection algorithm based on multi-slice CT images for lung cancer screening. In: Proc. of SPIE, vol. 5370, pp. 1083–1090 (2004)

  97. Enquobahrie A.A., Reeves A., Yankelevitz D.F. et al.: Automated detection of small pulmonary nodules in whole lung CT scans. Acad. Radiol. 14, 579–593 (2007)

    Article  Google Scholar 

  98. Zhao B., Gamsu G., Ginsberg M.S. et al.: Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. Appl. Clin. Med. Phys. 4, 248–260 (2003)

    Article  Google Scholar 

  99. Zhao, B., Ginsberg, M.S., Lefkowitz, R.A., et al.: Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans. In: Proc. of SPIE, vol. 5370, pp. 818–823 (2004)

  100. Wiemker, R., Rogalla, P., Zwartkruis, A., et al.: Computer aided lung nodule detection on high resolution CT data. In: Proc. SPIE Conf. Medical Imaging, vol. 4684, pp. 677–688. San Diego, CA (2002)

  101. Goo, J.M., Lee, J.W., Lee, H.J., et al.: Automated lung nodule detection at low-dose CT: Preliminary experience. Korean J. Radiol. 4 (2003)

  102. Itai Y., Hyoungseop K., Ishida T. et al.: A segmentation method of lung areas by using snakes and automatic detection of abnormal shadow on the areas. Int. J. Innov. Comput. Inf. Control 3, 277–284 (2007)

    Google Scholar 

  103. Nie S.-D., Li, L.-H., Chen, Z.-X.: A CI feature-based pulmonary nodule segmentation using three-domain mean shift clustering. In: International Conference on Wavelet Analysis and Pattern Recognition, pp. 223–227. IEEE Xplore, Beijing, China (2007)

  104. Murphy K., van Ginneken B., Schilham A.M. et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13, 757–770 (2009)

    Article  Google Scholar 

  105. Way T.W., Sahiner B., Chan H.P. et al.: Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med. Phys. 36, 3086–3098 (2009)

    Article  Google Scholar 

  106. Armato S.G., MacMahon H.: Automated lung segmentation and computer-aided diagnosis for thoracic CT scans. Acad. Radiol. 11, 1011–1021 (2004)

    Article  Google Scholar 

  107. Brown M.S., Goldin J.G., Suh R.D. et al.: Lung micronodules: automated method for detection at thin-section CT-initial experience. Radiology 226, 256–262 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, Deakin University, Geelong, VIC, 3217, Australia

    S. L. A. Lee & A. Z. Kouzani

  2. School of Mechanical Engineering, Adelaide University, North Terrace, Adelaide, SA, 5005, Australia

    E. J. Hu

Authors
  1. S. L. A. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Z. Kouzani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. J. Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Z. Kouzani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, S.L.A., Kouzani, A.Z. & Hu, E.J. Automated detection of lung nodules in computed tomography images: a review. Machine Vision and Applications 23, 151–163 (2012). https://doi.org/10.1007/s00138-010-0271-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-010-0271-2

Keywords

Search

Navigation