Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radiographs

  • Simranpreet KaurEmail author
  • Rahul Hooda
  • Ajay Mittal
  • Akashdeep
  • Sanjeev Sofat
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 712)


Lung Field Segmentation (LFS) is an indispensable step for detecting austere lung diseases in various computer-aided diagnosis. This paper presents a deep learning-based Convolutional Neural Network (CNN) for segmenting lung fields in chest radiographs. The proposed CNN network consists of three sets of convolutional-layer and rectified linear unit (ReLU) layer, followed by a fully connected layer. At each convolutional layer, 64 filters retrieve the representative features. Japanese Society of Radiological Technology (JSRT) dataset is used for training and validation. Test results have 98.05% average accuracy, 93.4% average overlap, 96.25% average sensitivity, and 98.80% average specificity. The obtained results are promising and better than many of the existing state-of-the-art LFS techniques.


Lung Field Segmentation (LFS) Convolutional Neural Network (CNN) Deep learning Chest radiography 


  1. 1.
    Van Ginneken, B., ter Haar Romeny, B.M., Viergever, M.A.: Computer-aided diagnosis in chest radiography: a survey. IEEE Trans. Med. Imaging 20(12), 1228–1241 (2001)CrossRefGoogle Scholar
  2. 2.
    Li, L., Zheng, Y., Kallergi, M., Clark, R.A.: Improved method for automatic identification of lung regions on chest radiographs. Acad. Radiol. 8(7), 629–638 (2001)Google Scholar
  3. 3.
    Duryea, J., Boone, J.M.: A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images. Med. Phys. 22(2), 183–191 (1995)CrossRefGoogle Scholar
  4. 4.
    Armato, S.G., Giger, M.L., MacMahon, H.: Automated lung segmentation in digitized posteroanterior chest radiographs. Acad. Radiol. 5(4), 245–255 (1998)Google Scholar
  5. 5.
    Ahmad, W.S.H.M.W., Zaki, W.M.D.W., Fauzi, M.F.A.: Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter. Biomed. Eng. Online 14(1), 1 (2015)Google Scholar
  6. 6.
    McNitt-Gray, M.F., Sayre, J.W., Huang, H.K., Razavi, M.: Pattern classification approach to segmentation of chest radiographs. In: Medical Imaging 1993, pp. 160–170. International Society for Optics and Photonics (1993)Google Scholar
  7. 7.
    Tsujii, O., Freedman, M.T., Mun, S.K.: Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network. In: Medical Imaging 1997, pp. 802–811. International Society for Optics and Photonics (1997)Google Scholar
  8. 8.
    Van Ginneken, B., ter Haar Romeny, B.M.: Automatic segmentation of lung fields in chest radiographs. Med. phys. 27(10), 2445–2455 (2000)Google Scholar
  9. 9.
    Vittitoe, N.F., Vargas-Voracek, R., Floyd Jr., C.E.: Markov random field modeling in posteroanterior chest radiograph segmentation. Med. Phys. 26(8), 1670–1677 (1999)CrossRefGoogle Scholar
  10. 10.
    Kalinovsky, A.A., Kovalev, V.: Lung image segmentation using deep learning methods and convolutional neural networks (2016)Google Scholar
  11. 11.
    van Ginneken, B., Frangi, A.F., Staal, J.J., ter Haar Romeny, B.M., Viergever, M.A.: Active shape model segmentation with optimal features. IEEE Trans. Med. Imaging 21(8), 924–933 (2002)Google Scholar
  12. 12.
    Xu, T., Mandal, M., Long, R., Basu, A.: Gradient vector flow based active shape model for lung field segmentation in chest radiographs. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2009, p. 3561. IEEE Engineering in Medicine and Biology Society (2009)Google Scholar
  13. 13.
    Lee, J.-S., Wu, H.-H., Yuan, M.-Z.: Lung segmentation for chest radiograph by using adaptive active shape models. Biomed. Eng.: Appl. Basis Commun. 22(02), 149–156 (2010)Google Scholar
  14. 14.
    Tao, X., Mandal, M., Long, R., Cheng, I., Basu, A.: An edge region force guided active shape approach for automatic lung field detection in chest radiographs. Comput. Med. Imaging Graph. 36(6), 452–463 (2012)CrossRefGoogle Scholar
  15. 15.
    Candemir, S., Jaeger, S., Palaniappan, K., Antani, S., Thoma, G.: Graph-cut based automatic lung boundary detection in chest radiographs. In: IEEE Healthcare Technology Conference: Translational Engineering in Health and Medicine, pp. 31–34 (2012)Google Scholar
  16. 16.
    Shi, Y., Qi, F., Xue, Z., Chen, L., Ito, K., Matsuo, H., Shen, D.: Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Trans. Med. Imaging 27(4), 481–494 (2008)CrossRefGoogle Scholar
  17. 17.
    Candemir, S., Jaeger, S., Palaniappan, K., Musco, J.P., Singh, R.K., Xue, Z., Karargyris, A., Antani, S., Thoma, G., McDonald, C.J.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2014)Google Scholar
  18. 18.
    van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med. Image Anal. 10(1), 19–40 (2006)CrossRefGoogle Scholar
  19. 19.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Simranpreet Kaur
    • 1
    Email author
  • Rahul Hooda
    • 2
  • Ajay Mittal
    • 1
  • Akashdeep
    • 1
  • Sanjeev Sofat
    • 2
  1. 1.UIET, Panjab UniversityChandigarhIndia
  2. 2.PEC University of TechnologyChandigarhIndia

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