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Journal of Medical Systems

, 43:332 | Cite as

Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features

  • Tanzila SabaEmail author
  • Ahmed Sameh
  • Fatima Khan
  • Shafqat Ali Shad
  • Muhammad SharifEmail author
Image & Signal Processing
  • 9 Downloads
Part of the following topical collections:
  1. Recent Advances in Deep Learning for Biomedical Signal Processing, Health Informatics and Computer Vision

Abstract

Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods.

Keywords

Cells Texture Benign VGG 19 SVM 

Notes

Acknowledgments

This work was supported by Research Project [Lung Cancer Diagnosis from CT Images]; Prince Sultan University; Saudi Arabia [SSP -18-5-03]”. Additionally, this work was partially supported by Artificial Intelligence and Data Analytics (AIDA) Lab, Prince Sultan University, Riyadh, Saudi Arabia.

Funding information

This work was supported by Research and Innovation Center through grant number SSP-18-5-03; Prince Sultan University, Riyadh, Saudi Arabia. This work was also partially supported by AI & Data Analytics Lab (AIDA), Prince Sultan University, Riyadh, Saudi Arabia.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest and all contribute equally in this work for results compilation and other technical support.

Ethical approval

This work is based on publicly available datasets. This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Not Applicable.

References

  1. 1.
    Gould, M. K., Fletcher, J., Iannettoni, M. D., Lynch, W. R., Midthun, D. E., Naidich, D. P., and Ost, D. E., Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines. Chest 132(3):108S–130S, 2007.PubMedCrossRefGoogle Scholar
  2. 2.
    Naqi, S., Sharif, M., Yasmin, M., and Fernandes, S. L., Lung nodule detection using polygon approximation and hybrid features from CT images. Current Medical Imaging Reviews 14(1):108–117, 2018.CrossRefGoogle Scholar
  3. 3.
    Messay, T., Hardie, R. C., and Rogers, S. K., A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3):390–406, 2010.PubMedCrossRefGoogle Scholar
  4. 4.
    Murphy, K., van Ginneken, B., Schilham, A. M., De Hoop, B., Gietema, H., and Prokop, M., 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(5):757–770, 2009.PubMedCrossRefGoogle Scholar
  5. 5.
    Veronesi, G., Bellomi, M., Mulshine, J. L., Pelosi, G., Scanagatta, P., Paganelli, G., Maisonneuve, P., Preda, L., Leo, F., and Bertolotti, R., Lung cancer screening with low-dose computed tomography: a non-invasive diagnostic protocol for baseline lung nodules. Lung Cancer 61(3):340–349, 2008.PubMedCrossRefGoogle Scholar
  6. 6.
    Kurihara, Y., Matsuoka, S., Yamashiro, T., Fujikawa, A., Matsushita, S., Yagihashi, K., and Nakajima, Y., MRI of pulmonary nodules. Am. J. Roentgenol. 202(3):W210–W216, 2014.CrossRefGoogle Scholar
  7. 7.
    Amin, J., Sharif, M., Yasmin, M., and Fernandes, S. L., A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn. Lett., 2017.Google Scholar
  8. 8.
    Nida, N., Sharif, M., Khan, M. U. G., Yasmin, M., and Fernandes, S. L., A framework for automatic colorization of medical imaging. IIOAB J 7:202–209, 2016.Google Scholar
  9. 9.
    Amin, J., Sharif, M., Yasmin, M., and Fernandes, S. L., Big data analysis for brain tumor detection: Deep convolutional neural networks. Futur. Gener. Comput. Syst. 87:290–297, 2018.CrossRefGoogle Scholar
  10. 10.
    Sobue, T., Moriyama, N., Kaneko, M., Kusumoto, M., Kobayashi, T., Tsuchiya, R., Kakinuma, R., Ohmatsu, H., Nagai, K., and Nishiyama, H., Screening for lung cancer with low-dose helical computed tomography: anti-lung cancer association project. J. Clin. Oncol. 20(4):911–920, 2002.PubMedCrossRefGoogle Scholar
  11. 11.
    White, C., New techniques in thoracic imaging. Boca Raton: CRC Press, 2001.CrossRefGoogle Scholar
  12. 12.
    Jiang, H., Ma, H., Qian, W., Gao, M., and Li, Y., An automatic detection system of lung nodule based on multigroup patch-based deep learning network. IEEE Journal of Biomedical and Health Informatics 22(4):1227–1237, 2018.PubMedCrossRefGoogle Scholar
  13. 13.
    Ali I, Hart G, Gunabushanam G, Liang Y, Muhammad W, Nartowt B, Kane M, Ma X, Deng J (2018) lung nodule Detection via Deep reinforcement learning. Front. Oncol. 8:108Google Scholar
  14. 14.
    Thawani, R., McLane, M., Beig, N., Ghose, S., Prasanna, P., Velcheti, V., and Madabhushi, A., Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41, 2018.PubMedCrossRefGoogle Scholar
  15. 15.
    Li, C., Zhu, G., Wu, X., and Wang, Y., False-positive reduction on lung nodules detection in chest radiographs by ensemble of convolutional neural networks. IEEE Access 6:16060–16067, 2018.CrossRefGoogle Scholar
  16. 16.
    M Naqi, S., and Sharif, M., Recent developments in computer aided diagnosis for lung nodule detection from CT images: a review. Current Medical Imaging Reviews 13(1):3–19, 2017.CrossRefGoogle Scholar
  17. 17.
    Dehmeshki, J., Ye, X., Lin, X., Valdivieso, M., and Amin, H., Automated detection of lung nodules in CT images using shape-based genetic algorithm. Comput. Med. Imaging Graph. 31(6):408–417, 2007.PubMedCrossRefGoogle Scholar
  18. 18.
    Mattoccia, S., Tombari, F., and Di Stefano, L., Efficient template matching for multi-channel images. Pattern Recogn. Lett. 32(5):694–700, 2011.CrossRefGoogle Scholar
  19. 19.
    Ukil, S., and Reinhardt, J. M., Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Trans. Med. Imaging 28(2):202–214, 2009.PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    Han, H., Li, L., Han, F., Song, B., Moore, W., and 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, 2015.PubMedCrossRefPubMedCentralGoogle Scholar
  21. 21.
    Gurcan, M. N., Sahiner, B., Petrick, N., Chan, H. P., Kazerooni, E. A., Cascade, P. N., and Hadjiiski, L., Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system. Med. Phys. 29(11):2552–2558, 2002.PubMedCrossRefPubMedCentralGoogle Scholar
  22. 22.
    Lee, Y., Hara, T., Fujita, H., Itoh, S., and Ishigaki, T., Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans. Med. Imaging 20(7):595–604, 2001.PubMedCrossRefGoogle Scholar
  23. 23.
    Dai, S., Lu, K., Dong, J., Zhang, Y., and Chen, Y., A novel approach of lung segmentation on chest CT images using graph cuts. Neurocomputing 168:799–807, 2015.CrossRefGoogle Scholar
  24. 24.
    Firmino, M., Angelo, G., Morais, H., Dantas, M. R., and Valentim, R., Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed. Eng. Online 15(1):2, 2016.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Ozekes, S., Osman, O., and 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):1–9, 2008.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Mukhopadhyay, S., A segmentation framework of pulmonary nodules in lung CT images. J. Digit. Imaging 29(1):86–103, 2016.PubMedCrossRefGoogle Scholar
  27. 27.
    Gupta, A., Saar, T., Martens, O., and Moullec, Y. L., Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Med. Phys. 45(3):1135–1149, 2018.PubMedCrossRefGoogle Scholar
  28. 28.
    Keshani, M., Azimifar, Z., Tajeripour, F., and Boostani, R., Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system. Comput. Biol. Med. 43(4):287–300, 2013.PubMedCrossRefGoogle Scholar
  29. 29.
    Ye, X., Lin, X., Dehmeshki, J., Slabaugh, G., and Beddoe, G., Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans. Biomed. Eng. 56(7):1810–1820, 2009.PubMedCrossRefGoogle Scholar
  30. 30.
    da Silva Sousa, J. R. F., Silva, A. C., de Paiva, A. C., and Nunes, R. A., Methodology for automatic detection of lung nodules in computerized tomography images. Comput. Methods Prog. Biomed. 98(1):1–14, 2010.CrossRefGoogle Scholar
  31. 31.
    Teramoto, A., and Fujita, H., Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter. Int. J. Comput. Assist. Radiol. Surg. 8(2):193–205, 2013.PubMedCrossRefGoogle Scholar
  32. 32.
    Brown, M. S., Lo, P., Goldin, J. G., Barnoy, E., Kim, G. H. J., McNitt-Gray, M. F., and Aberle, D. R., Toward clinically usable CAD for lung cancer screening with computed tomography. Eur. Radiol. 24(11):2719–2728, 2014.PubMedCrossRefGoogle Scholar
  33. 33.
    Riccardi, A., Petkov, T. S., Ferri, G., Masotti, M., and Campanini, R., Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med. Phys. 38(4):1962–1971, 2011.PubMedCrossRefGoogle Scholar
  34. 34.
    Gong, J., J-y, L., L-j, W., X-w, S., Zheng, B., and S-d, N., Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis. Physica Medica 46:124–133, 2018.PubMedCrossRefGoogle Scholar
  35. 35.
    Nibali, A., He, Z., and Wollersheim, D., Pulmonary nodule classification with deep residual networks. Int. J. Comput. Assist. Radiol. Surg. 12(10):1799–1808, 2017.PubMedCrossRefGoogle Scholar
  36. 36.
    Dou, Q., Chen, H., Yu, L., Qin, J., and Heng, P.-A., Multilevel contextual 3-d cnns for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7):1558–1567, 2017.PubMedCrossRefGoogle Scholar
  37. 37.
    Sun, W., Zheng, B., and Qian, W., Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput. Biol. Med. 89:530–539, 2017.PubMedCrossRefGoogle Scholar
  38. 38.
    Wang, C., Elazab, A., Wu, J., and Hu, Q., Lung nodule classification using deep feature fusion in chest radiography. Comput. Med. Imaging Graph. 57:10–18, 2017.PubMedCrossRefGoogle Scholar
  39. 39.
    Xie, Y., Zhang, J., Xia, Y., Fulham, M., and 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, 2018.CrossRefGoogle Scholar
  40. 40.
    Fernandes, S. L., Gurupur, V. P., Lin, H., and Martis, R. J., A Novel fusion approach for early lung cancer detection using computer aided diagnosis techniques. Journal of Medical Imaging and Health Informatics 7(8):1841–1850, 2017.CrossRefGoogle Scholar
  41. 41.
    Peterson, L. E., K-nearest neighbor. Scholarpedia 4(2):1883, 2009.CrossRefGoogle Scholar
  42. 42.
    Murphy, K. P., Naive bayes classifiers. Vol. 18. Vancouver: University of British Columbia, 2006, 60.Google Scholar
  43. 43.
    Burges, C. J., A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2):121–167, 1998.CrossRefGoogle Scholar
  44. 44.
    Breiman, L., Bagging predictors. Mach. Learn. 24(2):123–140, 1996.Google Scholar
  45. 45.
    Sun, Y., Liu, Z., Todorovic, S., and Li, J., Adaptive boosting for SAR automatic target recognition. IEEE Trans. Aerosp. Electron. Syst. 43(1):112–125, 2007.CrossRefGoogle Scholar
  46. 46.
    Swain, P. H., and Hauska, H., The decision tree classifier: Design and potential. IEEE Trans. Geosci. Electron. 15(3):142–147, 1977.CrossRefGoogle Scholar
  47. 47.
    Lim, Jae S., Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall, 469–476, 1990.Google Scholar
  48. 48.
    Otsu, N., "A Threshold Selection Method from Gray-Level Histograms." IEEE Transactions on Systems, Man, and Cybernetics. 9(1):62–66, 1979.CrossRefGoogle Scholar
  49. 49.
    Jolliffe, I. T. Principal Component Analysis. 2nd ed., Springer, 2002.Google Scholar
  50. 50.
    Despotović, I., Goossens, B., and Philips, W., MRI segmentation of the human brain: challenges, methods, and applications. Computational and Mathematical Methods in Medicine:1–23, 2015.CrossRefGoogle Scholar
  51. 51.
    Ojala, T., M. Pietikainen, and T. Maenpaa. “Multiresolution Gray Scale and Rotation Invariant Texture Classification With Local Binary Patterns.” IEEE Transactions on Pattern Analysis and Machine Intelligence. 24(7):971–987, 2002.CrossRefGoogle Scholar
  52. 52.
  53. 53.
    Nasir, M., Attique Khan, M., Sharif, M., Lali, I. U., Saba, T., and Iqbal, T., An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc. Res. Tech., 2018.Google Scholar
  54. 54.
    Lehmann, G., and Legland, D., Efficient N-dimensional surface estimation using Crofton formula and run-length encoding. Efficient N-Dimensional surface estimation using Crofton formula and run-length encoding, Kitware INC, 2012.Google Scholar
  55. 55.
    Krizhevsky, A., Sutskever, I., Hinton, G. E., Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012. pp 1097–1105Google Scholar
  56. 56.
    Ansari, G. J., Shah, J. H., Yasmin, M., Sharif, M., and Fernandes, S. L., A novel machine learning approach for scene text extraction. Futur. Gener. Comput. Syst. 87:328–340, 2018.CrossRefGoogle Scholar
  57. 57.
    Liaqat, A., Khan, M. A., Shah, J. H., Sharif, M., Yasmin, M., and Fernandes, S. L., Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection. Journal of Mechanics in Medicine and Biology 18(04):1850038, 2018.CrossRefGoogle Scholar
  58. 58.
    Sharif, M., Khan, M. A., Faisal, M., Yasmin, M., and Fernandes, S. L., A framework for offline signature verification system: Best features selection approach. Pattern Recogn. Lett., 2018.Google Scholar
  59. 59.
    Shah, J. H., Sharif, M., Yasmin, M., and Fernandes, S. L., Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recogn. Lett., 2017.Google Scholar
  60. 60.
    Tumer, K., and Ghosh, J., Error correlation and error reduction in ensemble classifiers. Connect. Sci. 8(3–4):385–404, 1996.CrossRefGoogle Scholar
  61. 61.
    Bishop, C. M. Pattern recognition and machine learning. springer, 2006.Google Scholar
  62. 62.
    Armato, III, S. G., McLennan, G., McNitt-Gray, M. F., Meyer, C. R., Yankelevitz, D., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., and MacMahon, H., Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3):739–748, 2004.PubMedCrossRefPubMedCentralGoogle Scholar
  63. 63.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015. pp 1–9Google Scholar
  64. 64.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and Bernstein, M., Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3):211–252, 2015.CrossRefGoogle Scholar
  65. 65.
    He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. pp 770–778.Google Scholar
  66. 66.
    Huang G, Liu Z, Van Der Maaten L, Weinberger KQ Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. pp 4700–4708Google Scholar
  67. 67.
    Chen, C.-H., Chang, C.-K., Tu, C.-Y., Liao, W.-C., Wu, B.-R., Chou, K.-T., Chiou, Y.-R., Yang, S.-N., Zhang, G., and Huang, T.-C., Radiomic features analysis in computed tomography images of lung nodule classification. PLoS One 13(2):e0192002, 2018.  https://doi.org/10.1371/journal.pone.0192002.s001.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Naqi, S. M., Sharif, M., and Yasmin, M., Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Int. J. Comput. Assist. Radiol. Surg.:1–13, 2018.Google Scholar
  69. 69.
    Naqi, S. M., Sharif, M., and Jaffar, A., Lung nodule detection and classification based on geometric fit in parametric form and deep learning. Neural Comput. & Applic.:1–19, 2018.Google Scholar
  70. 70.
    Bhatia, S., Sinha, Y., Goel, L., Lung Cancer Detection: A Deep Learning Approach. In: Soft Computing for Problem Solving. Springer, 2019, pp 699–705.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer and Information SciencesPrince Sultan UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer ScienceLuther CollegeDecorahUSA
  3. 3.Department of Computer ScienceCOMSATS University IslamabadIslamabadPakistan

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