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Robust Object Segmentation with Constrained Curve Embedding Potential Field

  • Gary H. P. Ho
  • Pengcheng Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3150)

Abstract

We have earlier introduced an implicit vector field representation for arbitrary number of curves in space, the curve embedding potential field (CEPF), and a general image segmentation strategy based on the detection of the CEPF distortion under the influence of vector-form image data [3]. In this paper, we present an improved CEPF framework which incorporates prior knowledge of the object boundary and has consistent object definition through a region growing process. The embedded implicit curves deform through the image- and model-induced changes of the CEPF, which evidently improves the segmentation accuracy under noisy and broken-edge situations. Further, the closure enforcement and the natural advection on the curves enhance the stability of CEPF evolution and the implementation is straightforward. Robust experimental results on cardiac and brain images are presented.

Keywords

Object Boundary Active Contour Model Curve Point Segmentation Accuracy Curve Element 
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.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22, 61–79 (1997)zbMATHCrossRefGoogle Scholar
  2. 2.
    Cohen, L.D.: On active contour models and balloons. CVGIP: Image Understanding 53, 211–218 (1991)zbMATHCrossRefGoogle Scholar
  3. 3.
    Ho, H.P., Shi, P.: Boundary finding with curve embedding potential field. In: 6th Medical Image Computing and Computer Assisted Intervention (2003)Google Scholar
  4. 4.
    Ho, H.P., Shi, P.: Domain partitioning level set surface for topology constrained multi-object segmentation. In: IEEE International Symposium on Biomedical Imaging (ISBI) (2004)Google Scholar
  5. 5.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1, 321–331 (1987)CrossRefGoogle Scholar
  6. 6.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front porpagation: a level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(2), 158–175 (1995)CrossRefGoogle Scholar
  7. 7.
    McInerney, T., Terzopoulos, D.: Topologically adaptable snakes. In: Fifth IEEE International Conference on Computer Vision, pp. 840–845 (1995)Google Scholar
  8. 8.
    Ventura, G., Xu, J.X., Belytschko, T.: A vector level set method and new discontinuity approximations for crack growth by EFG. International Journal for numerical methods in engineering 54, 923–944 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Xu, C., Prince, L.: Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gary H. P. Ho
    • 1
  • Pengcheng Shi
    • 1
  1. 1.Biomedical Research Laboratory, Department of Electrical and Electronic EngineeringHong Kong University of Science and TechnologyKowloon, Hong Kong

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