A Novel Algorithm for Identification of Body Parts in Medical Images

  • Jongan Park
  • Gwangwon Kang
  • Sungbum Pan
  • Pankoo Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


In this paper, we introduce an algorithm based on energy information obtained from Wavelet Transform for classification of medical images according to imaging modalities and body parts. Various medical image retrieval systems are available today that classify images according to imaging modalities, orientations, body parts or diseases. Generally these are limited to either some specific body part or some specific disease. Further, almost all of them deal with the DICOM imaging format. Our technique, on the other hand, can be applied to any of the imaging formats. The results are shown for JPEG images in addition to DICOM imaging format. We have used two types of wavelets and we have shown that energy obtained in either case is quite distinct for each of the body part.


Body Part Image Retrieval High Resolution Compute Tomography JPEG Image DICOM Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lehmann, T.M., Schubert, H., Keysers, D., Kohnen, M., Wein, B.B.: The IRMA code for unique classification of medical images. In: Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, Proceedings of SPIE, vol. 5033, pp. 440–451 (2003)Google Scholar
  2. 2.
    Liu, Y., Rothfus, W.E., Kanade, T.: Tech. Report CMU-RI-TR-98-04, Robotics Institute, Carnegie Mellon University (1998)Google Scholar
  3. 3.
    Shyu, C., Brodley, C., Kak, A., Kosaka, A., Aisen, A., Broderick, L.: ASSERT, A physi-cian-in-the-loop content-based image retrieval system for HRCT image databases. Computer Vision and Image Understanding 75(1/2), 111–132 (1999)CrossRefGoogle Scholar
  4. 4.
    Mojsilovic, A., Gomes, J.: Semantic based categorization, browsing and retrieval in medical image databases. In: Proc. Int. Conf. Image Processing, ICIP 2002, Rochester, New York (Sepember 2002)Google Scholar
  5. 5.
    Lehmann, T.M., Fischer, B., Güld, M.O., Thies, C., Keysers, D., Deselaers, T., Schubert, H., Wein, B.B., Spitzer, K.: The IRMA Reference Database and Its Use for Content-Based Image Retrieval in Medical Applications. In: Ammenwerth, E., Gaus, W., Haux, R., Lovis, C., Pfeiffer, K.P., Tilg, B., Wichmann, H.E. (eds.) (Hrsg): GMDS 2004 - Kooperative Versorgung - Vernetzte Forschung - Ubiquitäre Information, pp. 251–253. Verlag videel OHG, Niebüll (2004)Google Scholar
  6. 6.
    Lehmann, T.M., Güld, M.O., Thies, C., Fischer, B., Spitzer, K., Keysers, D., Ney, H., Kohnen, M., Schubert, H., Wein, B.B.: Content-based Image Retrieval in Medical Applications. Methods of Information in Medicine 43(4), 354–361 (2004)Google Scholar
  7. 7.
    Cohen, A.: Ondelettes, analyses multirésolution et traitement numérique du signal. Ph.D. thesis, University of Paris IX, Dauphine (1992)Google Scholar
  8. 8.
    Daubechies, I.: Ten lectures on wavelets, SIAM (1992)Google Scholar
  9. 9.
    Mallinckrodt Institute of Radiology (MIR),
  10. 10.
  11. 11.
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jongan Park
    • 1
  • Gwangwon Kang
    • 1
  • Sungbum Pan
    • 1
  • Pankoo Kim
    • 1
  1. 1.College of Electronics & Information EngineeringChosun UniversityGwangjuKorea

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