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A FAST AND ACCURATE NUMERICAL METHOD FOR MEDICAL IMAGE SEGMENTATION

  • Li, Yibao (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY) ;
  • Kim, Jun-Seok (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY)
  • Received : 2010.07.19
  • Accepted : 2010.08.09
  • Published : 2010.12.25

Abstract

We propose a new robust and accurate method for the numerical solution of medical image segmentation. The modified Allen-Cahn equation is used to model the boundaries of the image regions. Its numerical algorithm is based on operator splitting techniques. In the first step of the splitting scheme, we implicitly solve the heat equation with the variable diffusive coefficient and a source term. Then, in the second step, using a closed-form solution for the nonlinear equation, we get an analytic solution. We overcome the time step constraint associated with most numerical implementations of geometric active contours. We demonstrate performance of the proposed image segmentation algorithm on several artificial as well as real image examples.

Keywords

References

  1. S.M. Allen and J.W. Cahn, A microscopic theory for antiphase boundary motion and its application to antiphase domain coarsening, Acta Metallurgica, 27 (1979), 1085-1095. https://doi.org/10.1016/0001-6160(79)90196-2
  2. B. Appleton and H. Talbot, Globally optimal geodesic active contours, Journal of Mathematical Imaging and Vision, 23 (2005), 67-86. https://doi.org/10.1007/s10851-005-4968-1
  3. M. $Bene\breve{s}$, V. Chalupecky, and K. Mikula, Geometrical image segmentation by the Allen-Cahn equation, Applied Numerical Mathematics, 51 (2-3) (2004), 187-205. https://doi.org/10.1016/j.apnum.2004.05.001
  4. V. Caselles, F. Catte, T. Coll, and F. Dibos, A geometric model for active contours in image processing, Numerische Mathematik, 66 (1993), 1-31. https://doi.org/10.1007/BF01385685
  5. V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours, International Journal of Computer Vision, 22 (1) (1997), 61-79. https://doi.org/10.1023/A:1007979827043
  6. T. Chan and L. Vese, Active contours without edges, IEEE Transactions on image processing, 10 (2) (2001), 266-277. https://doi.org/10.1109/83.902291
  7. J. Hahn and C-O. Lee, Geometric attraction-driven flow for image segmentation and boundary detection, Journal of Visual Communication and Image Image Representation, 21 (2010), 56-66. https://doi.org/10.1016/j.jvcir.2009.10.005
  8. C. Li, C. Xu, C. Gu, and M.D. Fox, Level set evolution without re-initialization: a new variational formulation, IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, (2005), 430-436.
  9. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, Conformal curvature flows: From phase transitions to active vision, Archive for Rational Mechanics and Analysis, 134 (1996), 275-301. https://doi.org/10.1007/BF00379537
  10. A. Stuart and A.R. Humphries, Dynamical system and numerical analysis, Cambridge University Press, Cambridge, 1998.
  11. U. Trottenberg, C. Oosterlee, and A. Schuller, Multigrid, Academic Press, USA, 2001.
  12. L.A. Vese and T.F. Chan, A multiphase level set framevork for image segmentation using the mumford and shah model, International Journal of Computer Vision, 50 (3) (2002), 271-293. https://doi.org/10.1023/A:1020874308076
  13. A. Yezzi Jr, S. Kichenassamy, A. Kumar, P. Olver, and A. Tannenbaum, A geometric snake model for segmentation of medical imagery, IEEE Transaction on Medical Image, 16 (2) (1997), 199-209. https://doi.org/10.1109/42.563665

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