Advertisement

Contrast Enhanced Ultrasound Images Restoration

  • Adelaide Albouy-Kissi
  • Stephane Cormier
  • Bertrand Zavidovique
  • Francois Tranquart
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)

Abstract

In this paper, we propose a new anisotropic diffusion scheme to restore contrast enhanced ultrasound images for a better quantification of liver arterial perfusion. We exploit the image statistics to define a new edge stopping function. The method has been tested on liver lesions. The results show that the assessment of lesion vascularization from our process can potentially be used for the diagnostic of liver carcinoma.

Keywords

Contrast Enhanced Ultrasound Anisotropic Diffusion Coherence Liver Imaging Image Restoration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Quaia, E.: Assessment of tissue perfusion by contrast-enhanced ultrasound. European Radiology 21, 604–615 (2011)CrossRefGoogle Scholar
  2. 2.
    Kissi, A.A., Cormier, S., Pourcelot, L., Tranquart, F.: Hepatic lesions segmentation in ultrasound nonlinear imaging. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 5750, pp. 366–377 (2005)Google Scholar
  3. 3.
    Kissi, A., Cormier, S., Pourcelot, L., Tranquart, F.: Automatic lesions segmentation in ultrasound nonlinear imaging. In: ICIP (1), pp. 1153–1156 (2005)Google Scholar
  4. 4.
    Kaul, S., Pandian, N.G., Okada, R.D.: Contrast echocardiography in acute myocardial ischemia: I. in vivo determination of total left ventricular ’area at risk’. Journal of the American College of Cardiology 4, 1272–1282 (1984)CrossRefGoogle Scholar
  5. 5.
    Kaul, S., Pandian, N.G., Gillam, L.D.: Contrast echocardiography in acute myocardial ischemia. iii. an in vivo comparison of the extent of abnormal wall motion with the area at risk for necrosis. Journal of the American College of Cardiology 7, 383–392 (1986)CrossRefGoogle Scholar
  6. 6.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  7. 7.
    Black, M., Sapiro, G.: Edges as outliers: Anisotropic smoothing using local image statistics. In: Nielsen, M., Johansen, P., Fogh Olsen, O., Weickert, J. (eds.) Scale-Space 1999. LNCS, vol. 1682, pp. 259–270. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. Computing (suppl 11), 221–236 (1996)Google Scholar
  9. 9.
    Abd-Elmoniem, K.Z., Youssef, A.M., Kadah, Y.M.: Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion. IEEE Transactions on Biomedical Engineering 49, 997–1014 (2002)CrossRefGoogle Scholar
  10. 10.
    Scharr, H., Black, M.J., Haussecker, H.W.: Image statistics and anisotropic diffusion. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 840–847 (2003)Google Scholar
  11. 11.
    Weickert, J., Scharr, H.: A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. Journal of Visual Communication and Image Representation 13, 103–118 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adelaide Albouy-Kissi
    • 1
  • Stephane Cormier
    • 2
  • Bertrand Zavidovique
    • 3
  • Francois Tranquart
    • 4
  1. 1.Clermont UniversitéUniversité d’Auvergne, ISITClermontFrance
  2. 2.CReSTIC, Dept. Math-InfoUniversité de Reims Champagne ArdenneReims Cedex 2France
  3. 3.The Institute of Fundamental ElectronicsUniversité Paris XIOrsay CedexFrance
  4. 4.Research Centre, Bracco Imaging BVBracco Suisse SAPlan-les-OuatesSwitzerland

Personalised recommendations