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Achieving Fluid Detection by Exploiting Shadow Detection Methods

  • Matthias Noll
  • Julian Puhl
  • Stefan Wesarg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10549)

Abstract

Ultrasound provides a useful and readily available imaging tool. The big challenge in acquiring a good ultrasound image are possible shadow artefacts that hide anatomical structures. This applies in particular to 3D ultrasound acquisitions, because shadow artefacts may be recorded outside the visualized image plane. There are only a few automatic methods for shadow artefact detection. In our work we like to introduce a new shadow detection method that is based on an adaptive thresholding approach. The development was attempted, after existing methods had been extended to separate shadow and fluid regions. The entire detection procedure utilizes only the ultrasound scan line information and some basic knowledge about the ultrasound propagation inside the human body. Applying our method, the ultrasound operator can retrieve combined information about shadow and fluid locations, that may be invaluable for image acquisition or diagnosis. The method can be applied to conventional 2D as well as 3D ultrasound images.

Keywords

Ultrasound Shadow artefact Free fluid Detection 

References

  1. 1.
    Chan, T., Osher, S., Shen, J.: The digital tv filter and nonlinear denoising. IEEE Trans. Image Process. 10(2), 231–241 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Hellier, P., Coupe, P., Meyer, P., Morandi, X., Collins, D.: Acoustic shadows detection, application to accurate reconstruction of 3d intraoperative ultrasound. In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, ISBI 2008, pp. 1569–1572, May 2008Google Scholar
  3. 3.
    Ito, K., Sugano, S., Iwata, H.: Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4131–4136, October 2012Google Scholar
  4. 4.
    Karamalis, A., Wein, W., Klein, T., Navab, N.: Ultrasound confidence maps using random walks. Med. Image Anal. 16(6), 1101–1112 (2012). http://www.sciencedirect.com/science/article/pii/S1361841512000977 CrossRefGoogle Scholar
  5. 5.
    Michailovich, O.V., Tannenbaum, A.: Despeckling of medical ultrasound images. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 53(1), 64–78 (2006). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639001/ CrossRefGoogle Scholar
  6. 6.
    Noll, M., Puhl, J., Wesarg, S.: Enhanced shadow detection for 3D ultrasound. In: Deserno, T.M., Handels, H., Meinzer, H.-P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2014. I, pp. 234–239. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54111-7_45 CrossRefGoogle Scholar
  7. 7.
    Penney, G., Blackall, J., Hamady, M., Sabharwal, T., Adam, A., Hawkes, D.: Registration of freehand 3d ultrasound and magnetic resonance liver images. Med. Image Anal. 8(1), 81–91 (2004). http://view.ncbi.nlm.nih.gov/pubmed/14644148 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Visual Healthcare TechnologiesFraunhofer IGDDarmstadtGermany
  2. 2.GRIS, Technische Universität DarmstadtDarmstadtGermany

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