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Wavelet Compression/Reconstruction and Visualization of Pulmonary X-Ray Images for Achieving of Asbestosis Infected Patients Data

  • Ivica KuzmanićEmail author
  • Mirjana Vujović
  • Slobodan Marko Beroš
  • Igor Vujović
Chapter
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Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 4)

Abstract

An algorithm for reliable wavelet compression/reconstruction and visualization of pulmonary X-ray is presented in this chapter. Pulmonary X-rays are obtained by real patients from an asbestos factory. The aim is to make job easier to occupational medicine specialists and radiologists. Algorithm is primarily concerned for correct compression of the images to save space (digital memory space as well as space for storing X-ray films). Specialists must, according to law, save all X-ray images over 40 years. Instead of archiving X-ray films this algorithm allows saving of wavelet coefficients vectors on magnetic or optical storage. Independent radiologists confirmed that medical data is unchanged. Secondary concern is to emphasize possible asbestos-infected areas, which covers for visualization part of the work. Benefits are in monitoring of health condition, prevention of disease, early diagnostics, more reliable diagnostics, and saving space for achieving medical data.

Keywords

Wavelet image compression Preservation of medical data Comparison of wavelet families 

List of Abbreviations

DICOM

The Digital Imaging and Communications in Medicine

IEEE

Institute of Electrical and Electronic Engineers

JPEG

Joint Photographic Experts Group—file format

WT

Wavelet transform

DWT

Discrete wavelet transform

2D-DWT

Two-dimensional discrete wavelet transform

References

  1. 1.
    Pavlidis, T., Steiglitz, K.: The automatic counting of asbestos fibers in air samples. IEEE Trans. Comput. C-27(3), 258–261 (1978)Google Scholar
  2. 2.
    Paustenbach, D.J.: Bhopal, asbestos, and Love Canal… how they should affect engineering education. IEEE Technol. Soc. Mag. 6(1), 9–15 (1987)CrossRefGoogle Scholar
  3. 3.
    Petja, B.M., Twumasi, Y.A., Tengbeh, G.T.: The use of remote sensing to detect asbestos mining degradation in Mafefe and Mathabatha, South Africa. In: IEEE International Conference on Geoscience and Remote Sensing, pp. 1591–1593 (2006)Google Scholar
  4. 4.
    Petja, B.M., Twumasi, Y.A., Tengbeh, G.T.: Comparative analysis of reflectance spectroscopy and laboratory based assessment of asbestos pollution in the rehabilitated mining environment, South Africa. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1246–1249 (2007)Google Scholar
  5. 5.
    Ishizu, K., Takemura, H. et al.: Image processing of particle detection for asbestos qualitative analysis support method-particle counting by using color variance of background. In: SICE Annual Conference, pp. 3202–3207, Tokyo, 20–22 Aug 2008Google Scholar
  6. 6.
    Kawabata, K., Tsubota, Y. et al.: Development of an automatic polarized microscopic imaging system for asbestos qualitative analysis. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2009, pp. 1671–1676 (2009)Google Scholar
  7. 7.
    Bassani, C., Cavalli, R.M., et al.: Airborne emissivity data to map the urban materials to be checked for maintenance: The asphalt paving and asbestos cement roofing case studies. Joint Urban Remote Sensing Event 2009, 1–6 (2009)CrossRefGoogle Scholar
  8. 8.
    Vujović, M., Vujović, I., Kuzmanić, I.: New technologies and diagnosis of the professional asbestosis. Arch. Environ. Health 49(3), 251–258 (1998)Google Scholar
  9. 9.
    Vujović, I., Kuzmanić, I.: Histogram analysis of X-ray images and wavelet influence to the contained information. Med. Biol. Eng. Comput. 37(supp. 2), 1062–1063 (1999) Google Scholar
  10. 10.
    Vujović, I.: Digital image analysis and computer aid in diagnostics of asbestosis (in Croatian). Elektrotehnika 43(1–2), 17–22 (2000)Google Scholar
  11. 11.
    Vujović, M., Vujović, I., Kuzmanić, I.: The application of new technologies in diagnosing occupational asbestosis. Arch. Environ. Health 54(4), 245–252 (2003)Google Scholar
  12. 12.
    Vujović, I.: Application of wavelets in biomedical data processing with example in compression of X-rays of occupational asbestosis infected patients. MSc Thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Arhitecture (2004) Google Scholar
  13. 13.
    Cvitanović, S., Znaor, L.J., et al.: Malignant and non-malignant asbestos-related pleural and lung disease: 10-year follow-up study. Croat. Med. J. 44(5), 618–625 (2003)Google Scholar
  14. 14.
    Vujović, M.: Standardizing diagnostic criteria for assessment of asbestos- related occupational disease of the lung and pleura. Arch. Environ. Health 46, 445–449 (1995)Google Scholar
  15. 15.
    Simpson, S.G., Comstok, G.W.: Lung cancer and housing characteristics. Arch. Environ. Health 38, 248–252 (1983)CrossRefGoogle Scholar
  16. 16.
    Akay, M.: Time frequency and wavelets in biomedical signal processing. IEEE Press, New York (1998)zbMATHGoogle Scholar
  17. 17.
    Muyshondt, R.A., Mitra, S.: Visual fidelity of reconstructed radiographic images using wavelet transform coding and JPEG. In: 8th IEEE Symposium on Computer-Based Medical Systems, Lubbock, USA (1995)Google Scholar
  18. 18.
    Wang, H., Lai, S.L., Jiang, Y.H.: A comparative study of wavelet used in DICOM image compression. Chin. J. Med. Imaging Technol. 18(8), 827–829 (2002)Google Scholar
  19. 19.
    Heer, K., Reinfelder, H.E.: A comparison of reversible methods for data compression. In: Proceedings of SPIE “Medical Imaging IV”, SPIE, vol. 1233, pp. 354–365 (1990)Google Scholar
  20. 20.
    Said, A., Pearlman, W.A.: An image multiresolution representation for lossless and lossy compression. IEEE Trans. Image Process. 5(9), 1303–1310 (1996)CrossRefGoogle Scholar
  21. 21.
    Calderbank, A.R.; Daubechies, I., Sweldens, W., Yeo, B.L.: Lossless image compression using integer to integer wavelet transforms. In: Proceedings of International Conference on Image Processing ICIP, vol. 1, pp. 596–599. Washington, DC, USA, 26–29 Oct 1997Google Scholar
  22. 22.
    Boles, W.W.: A security system based on human iris identification using wavelet transform. Eng. Appl. Artif. Intell. 11(1), 77–85 (1998)CrossRefGoogle Scholar
  23. 23.
    Grosbois, R.: Image security and processing in the JPEG 2000 compressed domain. PhD Thesis, Université Paris, France (2003)Google Scholar
  24. 24.
    Dai, D.Q., Yuen, P.C.: Wavelet based discriminant analysis for face recognition. App. Math. Comput. 175(1), 307–318 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  25. 25.
    Mallat, S.: A Wavelet Tour of Signal Processing: The Sparse Way, 3rd edn. Academic Press, Burlington (2009)Google Scholar
  26. 26.
    Curent Status of DICOM Standard. http://www.dclunie.com/dicom-status/status.html. Accessed 14 Jan 2010
  27. 27.
    Pegasus Imaging Coorporation, Apollo 1.0. http://www.pegasusimaging.com. Accessed 23 July 2006
  28. 28.
    Guidelines for the Use of ILO International Classification of Radiographs of Pneumoconioses. International Labour Office, Geneva (1980)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ivica Kuzmanić
    • 1
    Email author
  • Mirjana Vujović
    • 2
  • Slobodan Marko Beroš
    • 3
  • Igor Vujović
    • 1
  1. 1.Faculty of Maritime StudiesUniversity of SplitSplitCroatia
  2. 2.Occupational Medicine Private PracticePločeCroatia
  3. 3.Faculty of Electrical Engineering, Mechanical Engineering and Naval ArchitectureUniversity of SplitSplitCroatia

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