Gallbladder Segmentation from 2–D Ultrasound Images Using Active Contour Models and Gradient Vector Flow

  • Marcin Ciecholewski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6930)

Abstract

Extracting the shape of the gallbladder from an ultrasonography (USG) image is an important step in software supporting medical diagnostics, as it allows superfluous information which is immaterial in the diagnostic process to be eliminated. In this project, several active contour models were used to segment the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps, and anatomical changes, such as folds of the gallbladder. The approximate edge of the gallbladder is found by applying one of the active contour models like the membrane and motion equation as well as the gradient vector flow model (GVF-snake). Then, the fragment of the image located outside the identified gallbladder contour is eliminated from the image. The tests carried out showed that the average value of the Dice similarity coefficient for the three active contour models applied reached 81.8%.

Keywords

Nodal Point Active Contour Motion Equation Gradient Vector Deformable Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aarnink, R.G., Pathak, S.D., de la Rosette, J.J., Debruyne, F.M., Kim, Y., et al.: Edge detection in prostatic ultrasound images using integrated edge maps. Ultrasonics 36, 635–642 (1998)CrossRefGoogle Scholar
  2. 2.
    Ames, W.F.: Numerical Methods for Partial Differential Equations, 3rd edn. Academic, New York (1992)MATHGoogle Scholar
  3. 3.
    Bodzioch, S.: Information reduction in digital image and its influence on the improvement of recognition process. Automatics, Semi-annual Journal of the AGH University of Science an Technology 8(2), 137–150 (2004)Google Scholar
  4. 4.
    Ciecholewski, M.: Gallbladder Segmentation in 2-D Ultrasound Images Using Deformable Contour Methods. In: Torra, V., Narukawa, Y., Daumas, M. (eds.) MDAI 2010. LNCS, vol. 6408, pp. 163–174. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Cvancarova, M., Albregtsen, T.F., Brabrand, K., Samset, E.: Segmentation of ultrasound images of liver tumors applying snake algorithms and GVF. International Congress Series (ICS), pp. 218–223 (2005)Google Scholar
  6. 6.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Englewood Cliffs (2008)Google Scholar
  7. 7.
    Hamou, A.K., Osman, S., El-Sakka, M.R.: Carotid Ultrasound Segmentation Using DP Active Contours. In: Kamel, M.S., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 961–971. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)CrossRefMATHGoogle Scholar
  9. 9.
    Leymarie, F., Levine, M.D.: Simulating the Grassfire Transform using an Active Contour Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(1), 56–75 (1992)CrossRefGoogle Scholar
  10. 10.
    Neuenschwander, W., Fua, P., Kuebler, O.: From Ziplock Snakes to Velcro Surfaces. In: Automatic Extraction of Man Made Objects from Aerial and Space Images, pp. 105–114. Birkhaeuser Verlag Basel, Monte Verita (1995)Google Scholar
  11. 11.
    Richard, W.D., Keen, C.G.: Automated texture-based segmentation of ultrasound images of the prostate. Comput. Med. Imaging Graph 20(3), 131–140 (1996)CrossRefGoogle Scholar
  12. 12.
    Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic segmentation of lumbar vertebrae on digitised radiographs using linked active appearance models. Proc. Medical Image Understanding and Analysis 2, 120–124 (2006)Google Scholar
  13. 13.
    Szczypiński, P., Strumiłło, P.: Application of an Active Contour Model for Extraction of Fuzzy and Broken Image Edges. Machine GRAPHICS & VISION 5(4), 579–594 (1996)Google Scholar
  14. 14.
    Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Xu, C., Prince, J.L.: Gradient vector flow: A new external force for snakes. In: IEEE Proc. Conf. on Computer Vision and Pattern Recognition, pp. 66–71 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcin Ciecholewski
    • 1
  1. 1.Institute of Computer ScienceJagiellonian UniversityKrakówPoland

Personalised recommendations