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Journal of Medical Systems

, 42:233 | Cite as

Inter-Patient Modelling of 2D Lung Variations from Chest X-Ray Imaging via Fourier Descriptors

  • Ali Afzali
  • Farshid Babapour Mofrad
  • Majid Pouladian
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

Detailed knowledge of anatomical lung variation is very important in medical image processing. Normal variations of lung consistent with the maintenance of pulmonary health and abnormal lung variations can be as a result of a pulmonary disease. Inter-patient lung variations can be due to the several factors such as sex, age, height, weight and type of disease. This study tries to show the inter-patient lung variations by using one of the shape-based descriptions techniques which is called Fourier descriptors. Shape-based description is an important approach to construct an object according to its parametric values. A different types of techniques are reported in the literature that aim to represent objects based on their shapes; each of these techniques has its cons and pros. Fourier descriptors, a simple yet powerful technique, has interesting properties such as rotational, scale, and translational invariance and these are powerful features for the recognition of two-dimensional connected shapes. In this paper, we use 380 CXR (Chest X-ray) images as a training set to construct the statistical mean model of lung contour. For modelling, the first step is evaluation of lung contour approximation and characterization to get the good spatial and frequency resolution. In the second step, all of the lung contours registered to show the variation and make a mean shape (i.e. lungs). And the final step is calculating the dispersion (i.e. covariance matrix) and analyzing by principle components. The proposed technique used to create the inter-patient statistical model and provide statistical parameters for application in segmentation, classification, 2D atlas based registration, etc. In this paper, we presented an approach for creating 2D modelling of human lungs from CXR image archives and reported some interesting statistical parameters to analysis the left and the right lung shape.

Keywords

Inter-patient lung variation 2D fourier descriptors Shape modelling Shape description Contour-based shape 

References

  1. 1.
    Sundaram, T. A., Avants, B. B., and Gee, J. C., A dynamic model of average lung deformation using capacity-based reparameterization and shape averaging of lung MR images. International conference on medical image computing and computer-assisted intervention. 1000–1007. Springer, Berlin, Heidelberg, 2004.CrossRefGoogle Scholar
  2. 2.
    Hayashi, K., Aziz, A., Ashizawa, K., Hayashi, H., Nagaoki, K., and Otsuji, H., Radiographic and CT appearances of the major fissures. Radiographics 21(4):861–874, 2001.CrossRefGoogle Scholar
  3. 3.
    Aldur, M. M., Denk, C. C., Celik, H. H., and Tasçioglu, A. B., An accessory fissure in the lower lobe of the right lung. Morphologie: Bulletin de l'Association des anatomistes 81(252):5–7, 1997.PubMedGoogle Scholar
  4. 4.
    Meenakshi, S., Manjunath, K. Y., and Balasubramanyam, V., Morphological variations of the lung fissures and lobes. Indian J. Chest Dis. All. Sci. 46:179–182, 2004.Google Scholar
  5. 5.
    Bansal, G. J., Digital radiography. A comparison with modern conventional imaging. Postgrad. Med. J. 82(969):425–428, 2006.CrossRefGoogle Scholar
  6. 6.
    Papaodysseus, C., Panagopoulos, T., Exarhos, M., Triantafillou, C., Fragoulis, D., and Doumas, C., Contour-shape based reconstruction of fragmented, 1600 BC wall paintings. IEEE Trans. Sign. Process. 50(6):1277–1288, 2002.CrossRefGoogle Scholar
  7. 7.
    Zahn, C. T., and Roskies, R. Z., Fourier descriptors for plane closed curves. IEEE Trans. Comput. 100(3):269–281, 1972.CrossRefGoogle Scholar
  8. 8.
    Kuhl, F. P., and Giardina, C. R., Elliptic Fourier features of a closed contour. Comput. Graph. Image Process. 18(3):236–258, 1982.CrossRefGoogle Scholar
  9. 9.
    Pinkowski, B., Fourier descriptors for characterizing object contour.. Proceedings of the international conference on signal processing applications and technology (ICSPAT’96), Boston, Massachusetts. 1007–1011. 1996.Google Scholar
  10. 10.
    Yip, R. K. K., Genetic Fourier descriptor for the detection of rotational symmetry. Imag Vision Comput. 25(2):148–154, 2007.CrossRefGoogle Scholar
  11. 11.
    Burger, W., and Burge, M. J.. Digital image processing: An algorithmic introduction using Java. Springer, 2016.Google Scholar
  12. 12.
    Ramakrishna, S., Ramalingam, M., Sampath Kumar, T. S., and Soboyejo, W. O., Biomaterials: A nano approach. CRC press, 2016.Google Scholar
  13. 13.
    Candemir, S., Jaeger, S., Palaniappan, K., Musco, J. P., Singh, R. K., Xue, Z., Karargyris, A., Antani, S., Thoma, G., and McDonald, C. J., Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imag. 33(2):577–590, 2014.CrossRefGoogle Scholar
  14. 14.
    Jaeger, S., Alexandros Karargyris, Sema Candemir, les folio, Jenifer Siegelman, Fiona Callaghan, Zhiyun Xue et al. "automatic tuberculosis screening using chest radiographs.". IEEE Trans. Med. Imag. 33(2):233–245, 2014.CrossRefGoogle Scholar
  15. 15.
    Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Komatsu, K.-i., Matsui, M., Fujita, H., Kodera, Y., and Doi, K., Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists' detection of pulmonary nodules. Am. J. Roentgenol. 174(1):71–74, 2000.CrossRefGoogle Scholar
  16. 16.
    Ginneken, V., Bram, M. B. S., and 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
  17. 17.
    Kovalev, V., Prus, A., and Vankevich, P., Mining lung shape from x-ray images. International workshop on machine learning and data Mining in Pattern Recognition. 554–568. Springer, Berlin, Heidelberg, 2009.CrossRefGoogle Scholar
  18. 18.
    Cosgriff, R. L., Identification of shape. Columbus, Rep: Ohio State Univ." Res. Foundation, 1960, 820–811.Google Scholar
  19. 19.
    Kazmi, I. K., You, L., and Zhang, J. J., A survey of 2d and 3d shape descriptors. Computer Graphics, imaging and visualization (cgiv), 2013 10th international conference. 1–10. IEEE, 2013.Google Scholar
  20. 20.
    Kunttu, I., Leena Lepistö, and Ari JE visa. "efficient Fourier shape descriptor for industrial defect images using wavelets.". Optic. Eng. 44(8):080503, 2005.CrossRefGoogle Scholar
  21. 21.
    El-ghazal, Akrem, O. Basir, and Saeid Belkasim. "A new shape signature for Fourier descriptors." In Image Process. 2007. ICIP 2007. IEEE Int. Conf., vol. 1, pp. 1-161. IEEE, 2007.Google Scholar
  22. 22.
    Zhang, G., Ma, Z.-m., Niu, L.-q., and Zhang, C.-m., Modified Fourier descriptor for shape feature extraction. J. Central South Univ. 19(2):488–495, 2012.CrossRefGoogle Scholar
  23. 23.
    Shen, L., Farid, H., and McPeek, M. A., Modeling three-dimensional MORPHOLOGICAL structures using spherical harmonics. Evolution 63(4):1003–1016, 2009.CrossRefGoogle Scholar
  24. 24.
    Yang, M., Kpalma, K., and Ronsin, J., A survey of shape feature extraction techniques: 43–90, 2008.Google Scholar
  25. 25.
    Zhang, D., and Guojun, L., A comparative study on shape retrieval using Fourier descriptors with different shape signatures. Proc. Int. Conf. Intell. Multimed. Dist. Educ. (ICIMADE01). 1–9. 2001.Google Scholar
  26. 26.
    Sidram, M. H., and Bhajantri, N. U., A novel shape signature of geometric mean of segmented centroid distance function to track the object through Fourier descriptors. Int. J. Comput. Appl. 83, no. 14, 2013.Google Scholar
  27. 27.
    Mofrad, F.B., Zoroofi, R. A., Chen, Y. W., Tehrani-Fard, A. A., Sato, Y. and Furukawa, A., Evaluation of liver shape approximation and characterization. Intell. Inform. Hiding Multimed. Sign. Process. 2009. IIH-MSP'09. Fifth Int. Conf. 1297-1300. IEEE. 2009.Google Scholar
  28. 28.
    Ehrhardt, J., and Lorenz, C. (Eds), 4D modeling and estimation of respiratory motion for radiation therapy. Berlin: Springer, 2013.Google Scholar
  29. 29.
    Kayalibay, B., Jensen, G. and van der Smagt, P., CNN-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056, 2017.Google Scholar
  30. 30.
    Yin, Y., and Yasuda, K., Similarity coefficient methods applied to the cell formation problem: A comparative investigation. Comput. Industr. Eng. 48(3):471–489, 2005.CrossRefGoogle Scholar
  31. 31.
    Westwood, J. D., A study about coefficients to estimate the error in biomechanical models used to virtually simulate the organ behaviors. Med. Meets Virt. Real. 19: NextMed 173:250, 2012.Google Scholar
  32. 32.
    Persoon, E., and King-Sun, F., Shape discrimination using Fourier descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 3:388–397, 1986.CrossRefGoogle Scholar
  33. 33.
    Richard, C. W., and Hemami, H., Identification of three-dimensional objects using Fourier descriptors of the boundary curve. IEEE Trans. Syst. Man Cybernet. 4:371–378, 1974.CrossRefGoogle Scholar
  34. 34.
    Bartolini, I., Ciaccia, P., and Patella, M., Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE Trans Pattern Analy. Mach. Intell. 27(1):142–147, 2005.CrossRefGoogle Scholar
  35. 35.
    Ahmed, F., Le, H. D. K., Olivier, J., and Boné, R., An active contour model with improved shape priors using Fourier descriptors.. VISAPP (1), 472–476. 2013.Google Scholar
  36. 36.
    Hižak, J., and Logožar, R., A derivation of the mean absolute distance in one-dimensional random walk. Tehnički glasnik 5(1):10–16, 2011.Google Scholar
  37. 37.
    Stegmann, M. B., and Gomez, D. D., A brief introduction to statistical shape analysis. Inform. Math. Model. Tech. Univ Denmark, DTU 15, no. 11, 2002.Google Scholar
  38. 38.
    Cootes, T., Baldock, E. R., and Graham, J., An introduction to active shape models. Image Process. Anal. 223–248, 2000.Google Scholar
  39. 39.
    Babapour Mofrad, F., Aghaeizadeh Zoroofi, R., Abbaspour Tehrani-Fard, A., Akhlaghpoor, S., Hori, M., Chen, Y.W. and Sato, Y., Statistical construction of a Japanese male liver phantom for internal radionuclide dosimetry. Radiat. Prot. Dosim., 141(2):140–148, 2010.CrossRefGoogle Scholar
  40. 40.
    Schneiter, F., Lip Contour Localization using Statistical Shape Models. PhD diss., Master Thesis Supervised by Gabriele Fanelli Computer Vision Institute, Department of Electrical Engineering & Information Technology, ETH Zurich, Switzerland Summer, 2009.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Medical Radiation EngineeringScience and Research Branch, Islamic Azad UniversityTehranIran
  2. 2.Department of Biomedical Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  3. 3.Research Center of Engineering in Medicine and Biology, Science and Research BranchIslamic Azad UniversityTehranIran

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