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Automated Lung Parenchyma Segmentation in the Presence of High Attenuation Patterns Using Modified Robust Spatial Kernel FCM

  • Shyla Raj
  • D. S. Vinod
  • Nagaraj Murthy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)

Abstract

Employing an accurate lung segmentation procedure is an essential stride in any Computer Aided Diagnosis (CAD) system designed for lung pathology evaluation, which significantly affects the performance of the system. In this paper a fully automated Modified Robust Spatial Kernel Fuzzy C Means (MRSKFCM) algorithm is used for extracting the lung parenchyma affected with Diffuse Lung Disease (DLD). The algorithm comprises of two steps, the Robust Spatial Kernel Fuzzy C Means (RSKFCM) is applied to acquire the coarse lung segmentation in the first step and in the second step the results of coarse segmentation is improved using Convex Hull (CH) algorithm and morphological operations. The algorithm used demonstrates high segmentation results with Dice index of 0.9057 ± 0.025, Jaccard Index of 0.8561 ± 0.029 and Accuracy 0.9864 ± 0.002. The execution time for each slice is approximately 2.5 s. Based on segmentation results and the evaluation time, the algorithm used can be considered as one of the desirable method for the extracting lung parenchyma in the CAD system.

Keywords

Diffuse lung diseases High attenuation patterns Lung segmentation MRSKFCM Convex hull TALISMAN 

References

  1. 1.
    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), 20 (2015)CrossRefGoogle Scholar
  2. 2.
    Aruna Kumar, S.V., Harish, B.S.: Segmenting medical images using computational intelligence technique. Int. J. Inf. Process. 9(1), 48–56 (2015)Google Scholar
  3. 3.
    Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. (TOMS) 22(4), 469–483 (1996)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Science & Business Media, Heidelberg (2013).  https://doi.org/10.1007/978-1-4757-0450-1zbMATHGoogle Scholar
  5. 5.
    Cover, T.M.: Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 3, 326–334 (1965)CrossRefzbMATHGoogle Scholar
  6. 6.
    Dalpiaz, G., Maffessanti, M.: Diffuse lung diseases. In: Guglielmi, G., Peh, W., Guermazi, A. (eds.) Geriatric Imaging, pp. 365–388. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-35579-0_16CrossRefGoogle Scholar
  7. 7.
    Dash, J.K., Madhavi, V., Mukhopadhyay, S., Khandelwal, N., Kumar, P.: Segmentation of interstitial lung disease patterns in HRCT images. In: SPIE Medical Imaging, p. 94142R. International Society for Optics and Photonics (2015)Google Scholar
  8. 8.
    Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.A., Müller, H.: Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227–238 (2012)CrossRefGoogle Scholar
  9. 9.
    Doi, K.: Current status and future potential of computer-aided diagnosis in medical imaging. Br. J. Radiol. 78(Suppl. 1), s3–s19 (2005)CrossRefGoogle Scholar
  10. 10.
    Haider, C., Bartholmai, B.J., Holmes, D., Camp, J., Robb, R.A.: Quantitative characterization of lung disease. Comput. Med. Imaging Graph. 29(7), 555–563 (2005)CrossRefGoogle Scholar
  11. 11.
    Hutchinson, J., Fogarty, A., Hubbard, R., McKeever, T.: Global incidence and mortality of idiopathic pulmonary fibrosis: a systematic review. Eur. Respir. J. 46(3), 795–806 (2015)CrossRefGoogle Scholar
  12. 12.
    Jayaram, M., Fleyeh, H.: Convex hulls in image processing: a scoping review. Am. J. Intell. Syst. 6(2), 48–58 (2016)Google Scholar
  13. 13.
    Korfiatis, P., Kalogeropoulou, C., Karahaliou, A., Kazantzi, A., Skiadopoulos, S., Costaridou, L.: Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med. Phys. 35(12), 5290–5302 (2008)CrossRefGoogle Scholar
  14. 14.
    Korfiatis, P., Skiadopoulos, S., Sakellaropoulos, P., Kalogeropoulou, C., Costaridou, L.: Automated 3d segmentation of lung fields in thin slice CT exploiting wavelet preprocessing. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 237–244. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74272-2_30CrossRefGoogle Scholar
  15. 15.
    Rogers, W., Ryack, B., Moeller, G.: Computer-aided medical diagnosis: literature review. Int. J. Biomed. Comput. 10(4), 267–289 (1979)CrossRefGoogle Scholar
  16. 16.
    Shi, Z., Zhou, P., He, L., Nakamura, T., Yao, Q., Itoh, H.: Lung segmentation in chest radiographs by means of Gaussian kernel-based FCM with spatial constraints. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery. FSKD 2009, vol. 3, pp. 428–432. IEEE (2009)Google Scholar
  17. 17.
    Sluimer, I., Prokop, M., Van Ginneken, B.: Toward automated segmentation of the pathological lung in CT. IEEE Trans. Med. Imaging 24(8), 1025–1038 (2005)CrossRefGoogle Scholar
  18. 18.
    Sluimer, I., Schilham, A., Prokop, M., Van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imaging 25(4), 385–405 (2006)CrossRefGoogle Scholar
  19. 19.
    Taha, A.A., Hanbury, A.: Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015)CrossRefGoogle Scholar
  20. 20.
    Uchiyama, Y., Katsuragawa, S., Abe, H., Shiraishi, J., Li, F., Li, Q., Zhang, C.T., Suzuki, K., et al.: Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med. Phys. 30(9), 2440–2454 (2003)CrossRefGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    Van Rikxoort, E.V., Van Ginneken, B.: Automatic segmentation of the lungs and lobes from thoracic CT scans. In: Proceedings of the 4th international Workshop Pulmonary Image Anal, pp. 261–268 (2011)Google Scholar
  23. 23.
    Wang, J., Li, F., Li, Q.: Automated segmentation of lungs with severe interstitial lung disease in CT. Med. Phys. 36(10), 4592–4599 (2009)CrossRefGoogle Scholar
  24. 24.
    Xu, T., 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
  25. 25.
    Zwirewich, C.V., Mayo, J.R., Müller, N.: Low-dose high-resolution CT of lung parenchyma. Radiol. 180(2), 413–417 (1991)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information Science and EngineeringSri Jayachamarajendra College of EngineeringMysuruIndia
  2. 2.Department of RadiologyJSS Medical CollegeMysuruIndia

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