Scale-Space 1999: Scale-Space Theories in Computer Vision pp 453-458 | Cite as

A Windows-Based User Friendly System for Image Analysis with Partial Differential Equations

  • Do Hyun Chung
  • Guillermo Sapiro
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1682)

Abstract

In this paper we present and briefly describe a Windows user- friendly system designed to assist with the analysis of images in general, and biomedical images in particular. The system, which is being made publicly available to the research community, implements basic 2D image analysis operations based on partial differential equations (PDE’s). The system is under continuous development, and already includes a large number of image enhancement and segmentation routines that have been tested for several applications.

Keywords

Active Contour Image Enhancement Active Contour Model Geodesic Curve Geodesic Active Contour 
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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References

  1. 1.
    L. Alvarez, P. L. Lions, and J. M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion,” SIAM J. Numer. Anal. 29, pp. 845–866, 1992.MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    M. Bertalmio, G. Sapiro, and G. Randall, “Morphing active contours: A geometric, topology-free, technique for image segmentation and tracking,” Proc. IEEE ICIP, Chicago, October 1998.Google Scholar
  3. 3.
    M. Black, G. Sapiro, D. Marimont, and D. Heeger, “Robust anisotropic diffusion,” IEEE Trans. Image Processing, March 1998.Google Scholar
  4. 4.
    V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Proc. Int. Conf. Comp. Vision’ 95, Cambridge, June 1995.Google Scholar
  5. 5.
    V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” International Journal of Computer Vision 22:1, pp. 61–79, 1997.MATHCrossRefGoogle Scholar
  6. 6.
    V. Caselles, G. Sapiro, and D. H. Chung, “Vector median filters, inf-sup operations, and coupled PDE’s: Theoretical connections,” ECE-University of Minnesota Technical Report, September 1998.Google Scholar
  7. 7.
    L. Cohen and R. Kimmel, “Global minimum for active contours models: A minimal path approach,” Int. J. of Computer Vision 24, pp. 57–78, 1997.CrossRefGoogle Scholar
  8. 8.
    M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision 1, pp. 321–331, 1988.CrossRefGoogle Scholar
  9. 9.
    R. Kimmel and J. A. Sethian, “Fast marching method for computation of distance maps,” LBNL Report 38451, UC Berkeley, February, 1996Google Scholar
  10. 10.
    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, pp. 275–301, 1996.MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    R. Malladi, J. A. Sethian and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE-PAMI 17, pp. 158–175, 1995.Google Scholar
  12. 12.
    S. J. Osher and J. A. Sethian, “Fronts propagation with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations,” Journal of Computational Physics 79, pp. 12–49, 1988.MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE-PAMI 12, pp. 629–639, 1990.Google Scholar
  14. 14.
    G. Sapiro, “Color snakes,” Computer Vision and Image Understanding 68:2, pp. 247–253, 1997.CrossRefMathSciNetGoogle Scholar
  15. 15.
    J. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision and Materials Sciences, Cambridge University Press, Cambridge-UK, 1996.Google Scholar
  16. 16.
    J. Shah, “Recovery of shapes by evolution of zero-crossings,” Technical Report, Math. Dept. Northeastern Univ. Boston MA, 1995.Google Scholar
  17. 17.
    D. Terzopoulos, A. Witkin, and M. Kass, “Constraints on deformable models: Recovering 3D shape and nonrigid motions,” AI 36, 1988.Google Scholar
  18. 18.
    J. N. Tsitsiklis, “Efficient algorithms for globally optimal trajectories,” IEEE Transactions on Automatic Control 40 pp. 1528–1538, 1995.MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    L. Vazquez, G. Sapiro, and G. Randall, “Segmenting neurons in electronic microscopy via geometric tracing,” Proc. IEEE ICIP, Chicago, October 1998.Google Scholar
  20. 20.
    J. Weickert, “Coherence-enhancing diffusion of color images,” Proc. VII National Symp. on Pattern Recognition and Image Analysis, pp. 239–244, Barcelona, Spain, 1997.Google Scholar
  21. 21.
    R. T. Whitaker, “Algorithms for implicit deformable models,” Proc. ICCV’95, pp. 822–827, Cambridge, June 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Do Hyun Chung
    • 1
  • Guillermo Sapiro
    • 1
  1. 1.Electrical and Computer EngineeringUniversity of MinnesotaMinneapolis

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