Skip to main content

Advertisement

Springer Nature Link
Log in

Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist’s experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a “second opinion” for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. World Health Organization. [Online] http://www.who.int/en/. Accessed 05 Sept 2017

  2. Global Cancer Observatory. [Online] http://gco.iarc.fr/. Accessed 12 Oct 2017

  3. Dela Cruz CS, Tanoue LT, Matthay RA: Lung cancer: epidemiology, etiology, and prevention. Clinics in Chest Medicine 32(4):605–644, 2011

    Article  Google Scholar 

  4. ABID Larbi: Lung cancer around the world and Arab countries. AMAAC Workshop, Algiers2011

  5. American Cancer Society (ACS). [Online] https://www.cancer.org/. Accessed 20 Dec 2018

  6. National Cancer Centre Singapore. [Online] https://www.nccs.com.sg/Pages/Home.aspx/. Accessed 12 Oct 2017

  7. National Cancer Institute (INCA). [Online] https://www.inca.gov.br/. Accessed 16 Oct 2017.

  8. Fleischner Society. [Online] https://fleischner.memberclicks.net/. Accessed 16 Oct 2017

  9. Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI: Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Transaction on Medical Imaging 22(10):1259–1274, 2003

    Article  Google Scholar 

  10. Golan R, Jacob C, Denzinger J: Lung nodule detection in CT images using deep convolutional neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 243–250, 2016. https://doi.org/10.1109/IJCNN.2016.7727205

  11. Cancer Research UK. [Online] https://www.cancerresearchuk.org/. Accessed 12 Oct 2017

  12. Godoy MC, Truong MT, Carter BW, Viswanathan C, De Groot P, Ko JP: Pitfalls in pulmonary nodule characterization. Seminars in Roentgenology 50(3):164-174, 2015

    Article  PubMed  Google Scholar 

  13. National Lung Screening Trial (NLST).[Online]https://www.cancer.gov/types/lung/research/nlst/. Accessed 25 Oct 2017

  14. MacMahon H, Austin JH, Gamsu G, Herold CJ, Jett JR, Naidich DP, Patz, Jr EF, Swensen SJ: Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 237(2):395–400, 2005

    Article  PubMed  Google Scholar 

  15. Lederlin M, Revel MP, Khalil A, Ferretti G, Milleron B, Laurenta F: Management strategy of pulmonary nodule in 2013. Diagnostic and Interventional Imaging 94(11):1081–1094, 2013

    Article  CAS  PubMed  Google Scholar 

  16. Rena O, Davoli F, Boldorini R, Roncon A, Baietto G, Papalia E, Turello D, Massera F, Casadio C: The solitary pulmonary nodule in patients with previous cancer history: results of surgical treatment. European Journal of Surgical Oncology 39(11):1248–1253, 2013

    Article  CAS  PubMed  Google Scholar 

  17. Dhara AK, Mukhopadhyay S, Khandelwal N: Computer-aided detection and analysis of pulmonary nodule from CT images: a survey. Technical Review IETE 29(4):265-275, 2012

    Article  Google Scholar 

  18. Attili AK, Kazerooni EA: Imaging of the solitary pulmonary nodule. In: Evidence-Based Imaging. New York: Springer, 2006. https://doi.org/10.1007/0-387-31216-1_23

  19. Sharma SK, Mohan A: Solitary Pulmonary Nodule: How and How Much to Investigate? The Association of Physicians of India, Ch. 109:824–832, medicine update, 2008

  20. Brandman S, Ko JP: Pulmonary nodule detection, characterization, and management with multi detector computed tomography. Journal of Thoracic Imaging 26(2):90–105, 2011

    Article  PubMed  Google Scholar 

  21. De Hoop B, Gietema H, Van de Vorst S, Murphy K, Van Klaveren RJ, Prokop M: Pulmonary ground-glass nodules: increase in mass as an early indicator of growth. Radiology 255(1):199–206, 2010

    Article  PubMed  Google Scholar 

  22. Sluimer I, Schilham A, Prokop M, Van Ginneken B: Computer analysis of computer tomography scan of the lung: a survey. IEEE Transactions on Medical Imaging 25(4):385–405, 2006

    Article  PubMed  Google Scholar 

  23. Doi K: Computer-aided diagnosis in radiological imaging: current status and future challenges. In: SPIE Proceedings of Medical Imaging .7497, pp 1A-1–1A-8, 2009. https://doi.org/10.1117/12.851178

  24. Devaki K, MuraliBhaskaran V: Study of computed tomography images of the lungs: a survey. In: IEEE International Conference on Recent Trends in Information Technology (ICRTIT), pp 837–842, 2011. https://doi.org/10.1109/ICRTIT.2011.5972308

  25. Lee SLA, Kouzani AZ, Hu EJ: Automated detection of lung nodules in computed tomography images: a review. Machine Vision and Applications 23(1):151–163, 2010

    Article  Google Scholar 

  26. Valente IR, Cortez PC, Neto EC, Soares JM, De Albuquerque VH, Tavares JM: Automatic 3D pulmonary nodule detection in CT images: a survey. Computer Methods and Programs in Biomedicine 124:91–107, 2015

    Article  PubMed  Google Scholar 

  27. Lee N, Laine AF, Mrquez G, Levsky JM, Gohagan JK: Potential of computer-aided diagnosis to improve CT lung cancer screening. IEEE Reviews in Biomedical Engineering 2:136–146, 2009

    Article  PubMed  Google Scholar 

  28. LIDC-IDRI. [Online] https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI. Accessed 16 Oct 2017

  29. Armato III SG et al.: Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3):739–748, 2004

    Article  Google Scholar 

  30. Tan M, Deklerck R, Cornelis J, Jansen B: Phased searching with NEAT in a time-scaled framework: experiments on a computer-aided detection system for lung nodules. Artificial Intelligence in Medicine 59(3):157–167, 2013

    Article  PubMed  Google Scholar 

  31. Gupta A, Märtens O, Moullec YL, Saar T: A tool for lung nodules analysis based on and morphological pperation. In: IEEE International Symposium on Intelligent Signal Processing Intelligent Signal Processing (WISP) Proceedings, pp 1–5, 2015. https://doi.org/10.1109/WISP.2015.7139186

  32. Armato, III SG et al.: The lung image database consortium (LIDC) and image database resources initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical Physics 38(2):915–931, 2011

    Article  PubMed  PubMed Central  Google Scholar 

  33. Hancock MC, Magnan JF: Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset. In: SPIE Proceedings of Medical Imaging, 10134, 25-1–25-12, 2017. https://doi.org/10.1117/12.2254446

  34. Han F, Song B, Ma H, Qian W, Liang Z: Risk prediction of small pulmonary nodules based on novel CT image texture markers. In: SPIE Proceedings of Medical Imaging, 10134, 3Q-1–3Q-6, 2017. https://doi.org/10.1117/12.2253486

  35. Hancock MC, Magnan JF: Lung nodule malignancy classification using only radiologist- quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods. SPIE Journal of Medical Imaging 3(4):044504-1-044504-1, 2016. https://doi.org/10.1117/1.JMI.3.4.044504

    Article  Google Scholar 

  36. Messay T, Hardie RC, Tuinstra TR: Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset. Medical Image Analysis 22(1):48–62, 2015

    Article  PubMed  Google Scholar 

  37. Paul R, Hawkins SH, Schabath MB, Gillies RJ, Hall LO, Goldgof DB: Predicting malignant nodules by fusing deep features with classical radiomics features. SPIE Journal of Medical Imaging 5(1):011021-1-011021-11, 2018. https://doi.org/10.1117/1.JMI.5.1.011021

    Article  Google Scholar 

  38. Van Ginneken B et al.: Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Medical Image Analysis 14(6):707–722, 2010

    Article  PubMed  Google Scholar 

  39. TIME. [Online] http://cmp.felk.cvut.cz/projects/LungTIME/, 2009. Accessed 14 Nov 2017

  40. ELCAP. [Online] http://www.via.cornell.edu/databases/lungdb.html/, 2013. Accessed 25 Nov 2017

  41. LISS. [Online] http://isc.cs.bit.edu.cn/MLMR/LISS.html/, 2015. Accessed 26 Nov 2017

  42. Qiu S, Wen D, Cui Y, Feng J: Lung Nodules Detection in CT Images Using Gestalt-Based Algorithm. Chinese Journal of Electronics IET 25(4):711–718, 2016

    Article  Google Scholar 

  43. Sangamithraa PB, Govindaraju S: Lung tumour detection and classification using EK- mean clustering. In: IEEE International Conference on Wireless Communications,Signal Processing and Networking (WiSPNET), pp 2201–2206, 2016. https://doi.org/10.1109/WiSPNET.2016.7566533

  44. Aggarwal T, Furqan A, Kalra K: Feature extraction and LDA based classification of lung nodules in chest CT scan images. In: IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 2201–2206, 2015. https://doi.org/10.1109/ICACCI.2015.7275773

  45. De Carvalho Filho AO, De Sampaio WB, Silva AC, De Paiva AC, Nunes RA, Gattass M: Automatic detection of Solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artificial Intelligence in Medicine 60(3):165–177, 2013

    Article  PubMed  Google Scholar 

  46. Soltaninejad S, Keshani M, Tajeripour F: Lung nodule detection by KNN classifier and active contour modelling and 3D visualization. In: IEEE International Symposium on Artificial Intelligence and Signal Processing (AISP), pp 440–445, 2012. https://doi.org/10.1109/AISP.2012.6313788

  47. Sun S, Gu W, Fan L, Ren H: Detect GGO nodule based on dot filter. In: IEEE International Conference on Biomedical Engineering and Biotechnology (iCBEB), pp 765–767, 2012. https://doi.org/10.1109/iCBEB.2012.129

  48. Choi WJ, Choi TS: Automated pulmonary nodule detection based on three-dimensional shape- based feature descriptor. Computer Methods and Programs in Biomedicine 113(1):37–54, 2013

    Article  PubMed  Google Scholar 

  49. Nagata R, Kawaguchi T, Miyake H: Automated detection of lung nodules in chest radiographs using a false-positive reduction scheme based on template matching. In: IEEE International Conference on BioMedical Engineering and Informatics (BMEI), pp 216–223, 2013. https://doi.org/10.1109/BMEI.2012.6512916

  50. Jirapatnakul AC, Fotin SV, Reeves AP, Biancardi AM, Yankelevitz DF, Henschke CI: Automated nodule location and size estimation using a multi- scale Laplacian of Gaussian filtering approach. In: IEEE Conference on Engineering in Medicine and Biology Society, pp 1028–1031, 2009. https://doi.org/10.1109/IEMBS.2009.5334683

  51. Kubo M et al.: A cad system for lung cancer based on low dose single-slice CT image. In: SPIE Proceedings of Medical Imaging 4684:1262–1269, 2002. https://doi.org/10.1117/12.467086

  52. Shao H, Cao L, Liu Y: A detection approach for solitary pulmonary nodules based on CT images In: IEEE Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, pp 1253 1257, 2013. https://doi.org/10.1109/ICCSNT.2012.6526151

  53. Punithavathy K, Ramya MM, Poobal S: Analysis of statistical texture features for automatic lung cancer detection in PET/CT images. In: IEEE International Conference on Robotics, Automation, Control and Embedded Systems (RACE), pp 1–5, 2015. https://doi.org/10.1109/RACE.2015.7097244

  54. Pei X, Guo H, Dai J: Computerized detection of lung nodules in CT images by use of multiscale filters and geometrical constraint region growing. In: IEEE International Conference on Conference on Bioinformatics and Biomedical Engineering, pp 1–4, 2010. https://doi.org/10.1109/ICBBE.2010.5517771

  55. Takemura S, Han X, Chen YW, Ito K, Nishikwa I, Ito M: Enhancement and detection of lung nodules with multiscale filters in CT images. In: IEEE International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp 717 720, 2008. https://doi.org/10.1109/IIH-MSP.2008.303

  56. Miyajima T, Tokisa T, Maeda S, Kim H, Tan JK, Ishikawa S, Murakami S, Aoki T: Classification of lung nodules on temporal subtraction image based on statistical features and improvement of segmentation accuracy. In: IEEE International Conference on Control, Automation and Systems, pp 1814–1817, 2012

  57. Dhara AK, Mukhopadhyay S: A hybrid preprocessing method using geometry based diffusion and selective enhancement filtering for pulmonary nodule detection. In: SPIE Proceedings of Medical Imaging, 8315, pp 2Y-1–2Y-6, 2012. https://doi.org/10.1117/12.911644

  58. Dhara AK, Mukhopadhyay S, Alam N, Khandelwal N: Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images. In: SPIE Proceedings of Medical Imaging, 8670, pp 0K-1–0K-6, 2013. https://doi.org/10.1117/12.2006970

  59. Hadavi N, Nordin MJ, Shojaeipour A: Lung cancer diagnosis using CT-scan images based on cellular learning automata. In: IEEE International Conference on Computer and Information Sciences (ICCOINS), pp 1–5, 2014. https://doi.org/10.1109/ICCOINS.2014.6868370

  60. Namin ST, Moghaddam HA, Jafari R, Esmaeil-Zadeh M, Gity M: Automated detection and classification of and classification of pulmonary nodules in 3D thoracic CT images. In: IEEE International Conference on Systems, Man and Cybernetics, pp 3774–3779, 2010. https://doi.org/10.1109/ICSMC.2010.5641820

  61. Suárez-Cuenca JJ, Tahoces PG, Souto M, Lado MJ, Remy-Jardin M, Remy J, Vidal JJ: Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images. Computers in Biology and Medicine 39(10):921–933, 2009

    Article  PubMed  Google Scholar 

  62. Leemput SVD, Dorssers F, Bejnordi BE: A novel spherical shell filter for reducing false positives in automatic detection of pulmonary nodules in thoracic CT scans. In: SPIE Proceedings of Medical Imaging, 9414, pp 2P-1–2P-6, 2015. https://doi.org/10.1117/12.2082298

  63. Wang D, Wang J, Du Y, Tang P: Adaptive solitary pulmonary nodule segmentation for digital radiography images based on random walks and sequential filter. IEEE Access 5:1460–1468, 2017

    Article  Google Scholar 

  64. Cuifang Li, Shengdong N, Yuanjun W, Xiwen S: Experimental investigation of fuzzy enhancement for nonsolid pulmonary nodules. In: IEEE Symposium on Robotics and Applications (ISRA), pp 756–759, 2012. https://doi.org/10.1109/ISRA.2012.6219301

  65. Xu F, Zhang WJ, Li XY, Xiao H, Peng SH, Nam HD, Zhang M-F: A 3D multi-scale block LBP filter for lung nodule enhancement based on the CT images. In: IEEE International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp 1376–1380, 2017. https://doi.org/10.1109/CISP-BMEI.2016.7852931

  66. De Silve Sousa JRF, Silva AC, De Paiva AC, Nunes RA: Methodology for automatic detection of lung nodules in computerized tomography images. Computer Methods and Programs Programs in Biomedicine 98(1):1–14, 2009

    Google Scholar 

  67. Cascio D, Magro R, Fauci F, Iacomi M, Raso G: Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models. Computers in Biology and Medicine 42(11):1098–1109, 2012

    Article  CAS  PubMed  Google Scholar 

  68. Santos AM, De C, Filho AO, Silva AC, De Paiva AC, Nunes RA, Gattass M: Automati detection of small lung nodules in 3D CT data using Gaussian mixture models. Tsallis entropy and SVM. Engineering Applications of Artificial Intelligence 36:27–39, 2014

    Article  Google Scholar 

  69. Nunzio GD, Tommasi E, Agrusti A, Cataldo R, Mitri ID, Favetta M, Maglio S, Massafra A, Quarta M, Torsello M, Zecca I, Bellotti R, Tangaro S, Calvini P, Camarlinghi N, Falaschi F, Cerello P, Oliva P: Automatic lung segmentation in CT images with accurate handling of the Hilar region. Journal of Digital Imaging 24(1):11–27, 2011

    Article  PubMed  Google Scholar 

  70. Zhang W, Wang X, Li X, Chen J: 3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets. Computers in Biology and Medicine 92:64-72, 2017

    Article  PubMed  Google Scholar 

  71. Fu L, Ma J, Ren Y, Han Y S, Zhao J: Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features. In: SPIE Proceedings of Proceedings of Medical Imaging, 10134, pp 0A-1–0A-8, 2017. https://doi.org/10.1117/12.2253995

  72. Gopi K, Selvakumar J: Lung tumor area recognition and classification using EK- mean clustering and SVM. In: IEEE International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2), pp 97–100, 2017. https://doi.org/10.1109/ICNETS2.2017.8067906

  73. Paing MP, Choomchuay S: Classification of margin characteristics from 3D pulmonary nodules. In: IEEE International Conference on Biomedical Engineering International (BMEiCON), pp 1–5, 2017. https://doi.org/10.1109/BMEiCON.2017.8229104

  74. Paing MP,Choomchuay S: Ground glass opacity (GGO) nodules detection from lung CT scans. In: IEEE International Symposium on Electronics and Smart Devices (ISESD), pp 230–235, 2018. https://doi.org/10.1109/ISESD.2017.8253338

  75. Javaid M, Javid M, Rehman MZ, Shah SI: A novel approach to CAD system for the detection of lung nodules in CT images. Computer Methods and Programs in Biomedicine 135:125–139, 2016

    Article  PubMed  Google Scholar 

  76. Rendon-Gonzalez E, Ponomaryov V: Automatic lung nodule segmentation and classification in CT images based on SVM. In: IEEE International Kharkiv Symposium on Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves (MSMW), pp 1–4, 2016. https://doi.org/10.1109/MSMW.2016.7537995

  77. Saien S, Pilevar AH, Moghaddam HA: Refinement of lung nodule candidates based on local geometric shape analysis and Laplacian of Gaussian kernels. Computers in Biology and Medicine 54:188–198, 2014

    Article  PubMed  Google Scholar 

  78. Choi WJ, Choi TS: False positive reduction for pulmonary nodule detection using two- dimensional principal component analysis. In: SPIE Proceedings of Applications of Digital Image Processing, 7443, pp 22-1–22-8, 2009. https://doi.org/10.1117/12.827252

  79. Choi WJ, Choi TS: Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Information Sciences 212:57–78, 2012

    Article  Google Scholar 

  80. Jianto P, Paik DS, Meng X, Roos JE, Rubin GD: Shape “break-and-repair” strategy and its application to automated medical image segmentation. IEEE Transactions on Visualization and Computer Graphics 17(1):115–124, 2010

    Article  Google Scholar 

  81. Messay T, Hardie RC, Rogers SK: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Medical Image Analysis 14(3):390-406, 2010

    Article  PubMed  Google Scholar 

  82. Kamra P,Vishraj R, Kanica, Gupta S: Performance comparison of image segmentation techniques for lung nodule detection in CT images. In: IEEE International Conference on Signal Processing, Computing and Control (ISPCC), pp 302–306, 2016. https://doi.org/10.1109/ISPCC.2015.7375045

  83. Wei Y, Shen G, Li JJ: A fully automatic method for lung parenchyma segmentation and repairing. Journal of Digital Imaging 26(3):483–495, 2012

    Article  CAS  PubMed Central  Google Scholar 

  84. Nurfauzi R, Nugroho HA, Ardiyanto I: Lung detection using adaptive border correction. In: IEEE International Conference on Science and Technology-Computer (ICST), pp 57–60, 2017. https://doi.org/10.1109/ICSTC.2017.8011852

  85. Li XY, Xu F, Hu X, Peng SH, Nam HD, Zhao J-M: Self-adapting threshold of pulmonary parenchyma. In: IEEE International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp 1429–1434, 2017. https://doi.org/10.1109/CISP-BMEI.2016.7852941

  86. Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G: Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Transactions on Biomedical Engineering 56(7):1810–1820, 2009

    Article  PubMed  Google Scholar 

  87. El-Regaily SA, Salem MAM, Aziz MHA, Roushdy MI: Lung nodule segmentation and detection in computed tomography. In: IEEE International Conference on Intelligent Computing and Information Systems (ICICIS), pp 72–78, 2018. https://doi.org/10.1109/INTELCIS.2017.8260029

  88. Gonga J, Liu JY, Wang LJ, Zheng B, Niea SD: Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier. Physica Medica 32(12):1502–1509, 2016

    Article  Google Scholar 

  89. Agarwal R, Shankhadhar A, Sagar RK: Detection of lung cancer using content based medical image retrieval. In: IEEE International Conference on Advanced Computing & Communication Technologies, pp 48–52, 2015. https://doi.org/10.1109/ACCT.2015.33

  90. Farahani FV, Ahmadi A, Zarandi MHF: Lung nodule diagnosis from CT images based on ensemble learning. In: IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp 1–7, 2015. https://doi.org/10.1109/CIBCB.2015.7300281

  91. Sun S, Li W, Kang Y: Lung nodule detection based on GA and SVM. In: IEEE International Conference on Biomedical Engineering and Informatics (BMEI), pp 96–100, 2016. https://doi.org/10.1109/BMEI.2015.7401480

  92. Cerello P: The MAGIC-5 CAD for nodule detection in low dose and thin slice lung CTs. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Spectrometers, Detectors and Associated Equipment 623(2):832–835, 2010

    Article  CAS  Google Scholar 

  93. Pal NR, Pal SK: A review on image segmentation techniques. Pattern Recognition 26(9):1277–1294, 2003

    Article  Google Scholar 

  94. Haralick RM, Shapiro LG: Image Segmentation Techniques. Proceedings of Applications of Artificial Intelligence II 0548:39-1–39-6, 1985

    Google Scholar 

  95. Phamy DL, Xu C, Prince JL: A survey of current methods in medical image segmentation. Annual Review of Biomedical Engineering 2:315–337, 2000

    Article  Google Scholar 

  96. Sharma N, Aggarwal LM: Automated medical image segmentation techniques. Journal of Medical physics/Association of Medical Physicists of India 35(1):3–14, 2010

    PubMed Central  Google Scholar 

  97. Jung J, Hong H, Goo JM: Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal. Computers in Biology and Medicine 92:128–138, 2017

    Article  PubMed  Google Scholar 

  98. Badura P, Pietka E: Soft computing approach to 3D lung nodule segmentation in CT. Computers in Biology and Medicine 53:230–243, 2014

    Article  CAS  PubMed  Google Scholar 

  99. Keshani M, Azimifar Z, Tajeripour F, Boostani R: Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Computers in Biology and Medicine 43(4):287–300, 2013

    Article  PubMed  Google Scholar 

  100. Sun S, Guo Y, Guan Y, Ren H, Fan L, Kang Y: Juxta-vascular nodule segmentation based on flow entropy and geodesic distance. IEEE Journal of Biomedical and Health Informatics 18(4):1355–1362, 2014

    Article  PubMed  Google Scholar 

  101. Mukhopadhyay S: A segmentation framework of pulmonary nodules in lung CT images. Journal of Digital Imaging 29:86–103, 2015

    Article  PubMed Central  Google Scholar 

  102. Narayanan BN, Hardie RC, Kebede TM, Sprague MJ: Optimized feature selection-based clustering approach for computer-aided detection of lung nodules in different modalities. Pattern Analysis and Applications 22(2):559–571, 2017

    Article  Google Scholar 

  103. Aresta G, Cunha A, Campilho A: Detection of juxta-pleural lung nodules in computed tomography images. In: SPIE Proceedings of Medical Imaging, 10134, pp 3N-1–3N-7, 2017. https://doi.org/10.1117/12.2252022

  104. Mehre SA, Mukhopadhyay S, Dutta A, Harsha NC, Dhara AK, Khandelwal N: An automated lung nodule detection system for CT images using synthetic minority oversampling. In: SPIE Proceedings of Medical Imaging, 9785, pp 0H-1–0H-8, 2016. https://doi.org/10.1117/12.2216357

  105. Suárez-Cuenca JJ, Guo W, Li Q: Automated detection of pulmonary nodules In CT: false positive reduction by combining multiple classifiers. In: SPIE Proceedings of Medical Imaging, 7963, pp 38-1–38-6, 2011. https://doi.org/10.1117/12.878793

  106. Nithila EE, Kumar SS: Automatic detection of solitary pulmonary nodules using swarm intelligence optimized neural networks on CT images. Engineering Science and Technology, an an International Journal 20(3):1192–1202, 2016

    Article  Google Scholar 

  107. Farahani FV, Ahmadi A, Zarandi MHF: Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning. Mathematics and Computers in Simulation 149:48–68, 2018

    Article  Google Scholar 

  108. Netto SMB, Silva AFC, Nunes RA, Gattass M: Automatic segmentation of lung nodules with growing neural gas and support vector machine. Computers in Biology and Medicine 42(11):1110–1121, 2012

    Article  Google Scholar 

  109. Hosseini R, Qanadli SD, Barman S, Mazinani M, Ellis T, Dehmeshki J: An automatic approach for learning and tuning Gaussian interval type-2 fuzzy membership functions applied to lung CAD classification system. IEEE Transactions on Fuzzy Systems 20(2):224–234, 2011

    Article  Google Scholar 

  110. Antonelli M, Cococcioni M, Lazzerini B, Marcelloni F: Computer-aided detection of lung nodules based on decision fusion techniques. Journal of Pattern Analysis and Applications 14(3):295–310, 2011

    Article  Google Scholar 

  111. Gong J, Liu JY, Wang LJ, Sun XW, Zheng B, Nie SD: Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Physica Medica 46:124–133, 2018

    Article  PubMed  Google Scholar 

  112. Farag A, Ali A, Graham J, Farag A, Elshazly S, Falk R: Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp 169–172, 2011. https://doi.org/10.1109/ISBI.2011.5872380

  113. Alam M, Sankaranarayanan G, Devarajan V: Lung nodule detection and segmentation using a patch-based multi-atlas method. In: IEEE International Conference on Computational Science and Computational Intelligence (CSCI), pp 23–28, 2017. https://doi.org/10.1109/CSCI.2016.0012

  114. Assefa M, Faye I, Malik AS, Shoaib M: Lung nodule detection using multi- resolution analysis. In: IEEE International Conference on Complex Medical Engineering (CME), pp 457–461, 2013. https://doi.org/10.1109/ICCME.2013.6548290

  115. Jaffar MA, Siddiqui AB, Mushtaq M: Ensemble classification of pulmonary nodules using gradient intensity feature descriptor and differential evolution. Cluster Computing 21(1):393–407, 2017

    Article  Google Scholar 

  116. Novo J, Gonçalves L, Mendonça AM, Campilho A: 3D lung nodule candidate detection in multiple scales. In: IEEE International Conference on Machine Vision Applications (MVA), pp. 61–64, 2015 doi: https://doi.org/10.1109/MVA.2015.7153133

  117. Chen B, Kitasaka T, Honma H, Takabatake H, Mori M, Natori H, Mori K: Automatic segmentation of solitary pulmonary nodules based on Local intensity structure analysis and 3D neighborhood features in 3D chest CT Images. In: SPIE Proceedings of Medical Imaging, 8315, pp 38-1–38-8, 2012. https://doi.org/10.1117/12.911782

  118. Retico A, Bagagli F, Camarlinghi N, Carpentieri C, Fantacci ME, Gori I: A voxel-based neural approach (VBNA) to identify lung Nodules in the ANODE09 study. In: SPIE Proceedings of Medical Imaging, 7260, pp 1S-1–1S-8, 2009. https://doi.org/10.1117/12.811721

  119. Murphy K, Van Ginneken B, Schilham AM, De Hoop BJ, Gietema HA, Prokop M: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Medical Image Analysis 13(5):757–770, 2009

    Article  CAS  PubMed  Google Scholar 

  120. Froz BR, De Carvalho Filho AO, Silva AC, De Paiva AC, Nunes RA, Gattass M: Lung nodule classification using artificial crawlers, directional texture and support vector machine. Expert Systems with Applications 69(12):176–188, 2016

    Google Scholar 

  121. Han H, Li L, Han F, Song B, Moore W, Liang Z: Fast and adaptive detection of pulmonary nodules in thoracic CT images using a hierarchical vector quantization scheme. IEEE Journal of Biomedical and Health Informatics 19(2):648–659, 2014

    Article  PubMed  PubMed Central  Google Scholar 

  122. Yuan D, Du W, Duan X, Wang J, Ma Y, Zhang, H: Detection of slices including a ground-glass opacity nodule in CT volume data with semi-supervised learning. In: IEEE Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing (SNPD), pp 557–561, 2017. https://doi.org/10.1109/SNPD.2017.8022778

  123. Taşci E, Uğur A: Shape and texture based novel features for automated Juxtapleural nodule detection in lung CTs. Journal of Medical Systems 39–46, 2015

  124. Wang Q, Kang W, Wu C, Wang B: Computer-aided detection of lung nodules by SVM based on 3D matrix patterns. Clinical Imaging 37(1):62–69, 2013

    Article  PubMed  Google Scholar 

  125. Retico A, Fantacci ME, Gori I, Kasae P, Golosio B, Piccioli A, Cerello P, De Nunzio G, Tangaro S: Pleural nodule identification in low-dose and thin-slice lung computed tomography. Computers in Biology and Medicine 39(12):1137–1144, 2009

    Article  CAS  PubMed  Google Scholar 

  126. Sahoo PK, Soltani S, Wong AKC: A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing 41(2):233–260, 2004

    Article  Google Scholar 

  127. Freixenet J, Muñoz X, Raba D, Martí J, Cufí X: Yet another survey on image segmentation: region and boundary information integration. In: European Conference on Computer Vision (ECCV), Springer, pp 408–422, 2002. https://doi.org/10.1007/3-540-47977-5_27

    Chapter  Google Scholar 

  128. McInerney T, Terzopoulos D: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2):91–108, 2002

    Article  Google Scholar 

  129. Yokota K, Maeda S, Kim H, Tan JK, Ishikawa S, Tachibana R, Hirano Y, Kido S: Automatic detection of GGO regions on CT images in LIDC dataset based on statistical features. In: IEEE Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS), pp 1374–1377, 2015. https://doi.org/10.1109/SCIS-ISIS.2014.7044692

  130. Wang H, Guo XH, Jia ZW, Li HK, Liang ZG, Li KC, He Q: Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. European Journal of Radiology 74(1):124-129, 2009

    Article  PubMed  Google Scholar 

  131. Yu SJ, Wantroba JS, Raicu DS, Furst JD, Channin DS, Armato III SG: A study on the effect of CT imaging acquisition parameters on lung nodule image interpretation. In: SPIE Proceeding of Medical Imaging, 7263, 1R-1–1R-8, 2009. https://doi.org/10.1117/12.813695

  132. Dhara AK, Mukhopadhyay S, Khandelwal N: 3D texture analysis of solitary pulmonary nodules using co-occurrence matrix from volumetric lung CT images. In: SPIE Proceedings of Medical Imaging, 8670, pp 39-1–39-6, 2013. https://doi.org/10.1117/12.2007016

  133. Ciompi F, Jacobs C, Scholten ET, Wille MM, De Jong PA, Prokop M, Van Ginneken B: Bag-of-frequencies: a descriptor of pulmonary nodules in computed tomography images. IEEE Journal of Biomedical and Health Informatics 34(4):962–973, 2014

    Google Scholar 

  134. Vapnik VN: Statistical Learning Theory. USA: Wiley-Interscience, 1998

    Google Scholar 

  135. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van der Laak Jeroen AWM, Ginneken BV, Sanchez CI: A survey on deep learning in medical image analysis. Medical Image Analysis 42:60–88, 2017

    Article  PubMed  Google Scholar 

  136. Wong KKL, Wang L, Wang D: Recent developments in machine learning for medical imaging applications. Computerized Medical Imaging and Graphics 57:1–3, 2017

    Article  PubMed  Google Scholar 

  137. Bruijne M: Machine learning approaches in medical image analysis: from detection to diagnosis. Medical Image Analysis 33:94–97, 2016

    Article  PubMed  Google Scholar 

  138. Suzuki K: Overview of deep learning in medical imaging. Radiological Physics and Technology 10(3):257–273, 2017

    Article  PubMed  Google Scholar 

  139. Tajbakhsh N, Suzuki K: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs. Pattern Recognition 63:476–486, 2016

    Article  Google Scholar 

  140. Hasegawa A, Benedict Lo SC, Freedman MT, Mun SK: Convolution neural-network-based detection of lung structures. In: SPIE Proceedings of Medical Imaging, 2167, pp 654–662, 1994. https://doi.org/10.1117/12.175101

  141. Lin JS, Ligomenides PA, Lo SCB, Hasegawa A, Freedman MT, Mun SK: An application of convolution neural networks: reducing false-positives in lung nodule detection. In: IEEE Proceedings of 1994 IEEE Nuclear Science Symposium - NSS'94, pp 1842–1846, 1994. https://doi.org/10.1109/NSSMIC.1994.474706

  142. Lo SB, Lou SA, Lin JS, Freedman MT, Chien MV, Mun SK: Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Transactions on Medical Imaging 14(4):711–718, 1995

    Article  CAS  PubMed  Google Scholar 

  143. Dumoulin V, Visin F: A guide to Convolution Arithmetic for Deep Learning, 2018. https://arxiv.org/pdf/1603.07285.pdf/. Accessed 8 Feb 2018

  144. http://cs231n.stanford.edu/index.html. Accessed 9 Feb 2018

  145. http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/. Accessed 9 Feb 2018

  146. Jiang F, Grigorev A, Rho S, Tian Z, Fu YS, Jifara W, Adil K, Liu S: Medical image semantic segmentation based on deep learning. Neural Computing and Applications 29(5):1257–1265, 2017

    Article  Google Scholar 

  147. Yan X, Pang J, Qi H, Zhu Y, Bai C, Geng X, Liu M, Terzopoulos D, Ding X: Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: a comparison between 2D and 3D strategies. Asian Conference on Computer Vision (ACCV), Springer, vol. 10118, pp 91–101, 2017. https://doi.org/10.1007/978-3-319-54526-4_7

    Chapter  Google Scholar 

  148. Tan J, Huo Y, Liang Z, Li L: Apply convolutional neural network to lung nodule detection: recent progress and challenges. International Conference on Smart Health, pp. 214–222, 2017, Springer doi: https://doi.org/10.1007/978-3-319-67964-8_21

    Chapter  Google Scholar 

  149. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J: Multi- crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognition 61:663–673, 2016

    Article  Google Scholar 

  150. Setio AAA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, Wille MMW, Naqibullah M, Sánchez CI, Van Ginneken B: Pulmonary nodule detection in CT images: f alse positive reduction using multi-view convolutional networks. IEEE Transactions on Medical Imaging 35(5):1160–1169, 2016

    Article  PubMed  Google Scholar 

  151. Ciompi F, De Hoop B, Van Riel SJ, Chung K, Scholten ET, Oudkerk M, De Jong PA, Prokop M, Van Ginneken B: Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis 26(1):195–202, 2015

    Article  PubMed  Google Scholar 

  152. Dou Q, Chen H, Yu L, Qin J, Heng PA: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Transactions on Biomedical Engineering 64(7):1558–1567, 2016

    Article  PubMed  Google Scholar 

  153. Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J: Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Medical Image Analysis 40:172–183, 2017

    Article  PubMed  PubMed Central  Google Scholar 

  154. Da Silva GLF, Da Silve Neto OP, Silva AC, De Paiva AC, Gattass M: Lung nodules diagnosis based on evolutionary convolutional neural network. Multimedia Tools and Applications 76(18):19039–19055, 2017

    Article  Google Scholar 

  155. Yuan J, Liu X, Hou F, Qin H, Hao A: Hybrid-feature-guided lung nodule type classification on CT images. Computers & Graphics 70:288–299, 2017

    Article  Google Scholar 

  156. Xie Y, Zhang J, Xia Y, Michael F, Zhang Y: Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion 42:102–110, 2017

    Article  Google Scholar 

  157. Liu X, Hou F, Qin H, Hao A: Multi-view multi-scale CNNs for lung nodule type classification from CT images. Pattern Recognition 77:262–275, 2018

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Supreme Knowledge Foundation Group of Institutions, Hooghly, 712139, India

    Amitava Halder

  2. Electrical Engineering Department, Jadavpur University, Kolkata, 700032, India

    Debangshu Dey

  3. EKO CT & MRI Scan Centre, Medical College and Hospitals Campus, Kolkata, 700073, India

    Anup K. Sadhu

Authors
  1. Amitava Halder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debangshu Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anup K. Sadhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amitava Halder.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Halder, A., Dey, D. & Sadhu, A.K. Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J Digit Imaging 33, 655–677 (2020). https://doi.org/10.1007/s10278-020-00320-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-020-00320-6

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy