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Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5534))

Abstract

In this paper, we present a novel graph construction method and demonstrate its usage in a broad range of applications starting from a relatively simple single-surface segmentation and ranging to very complex multi-surface multi-object graph based image segmentation. Inspired by the properties of electric field direction lines, the proposed method for graph construction is inherently applicable to n-D problems. In general, the electric field direction lines are used for graph “column” construction. As such, our method is robust with respect to the initial surface shape and the graph structure is easy to compute. When applied to cross-surface mapping, our approach can generate one-to-one and every-to-every vertex correspondent pairs between the regions of mutual interaction, which is a substantially better solution compared with other surface mapping techniques currently used for multi-object graph-based image segmentation.

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References

  1. Wu, X., Chen, D.Z.: Optimal net surface problem with applications. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 1029–1042. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Li, K., Millington, S., Wu, X., Chen, D.Z., Sonka, M.: Simultaneous segmentation of multiple closed surfaces using optimal graph searching. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 406–417. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal surface segmentation in volumetric images – a graph-theoretic approach. IEEE Trans. Pattern Anal. and Machine Intelligence 28(1), 119–134 (2006)

    Article  Google Scholar 

  4. Zhao, F., Zhang, H., Walker, N.E., Yang, F., Olszewski, M.E., Wahle, A., Scholz, T., Sonka, M.: Quantitative analysis of two-phase 3D+time aortic MR images. SPIE Medical Imaging, vol. 6144, pp. 699–708 (2006)

    Google Scholar 

  5. Haeker, M., Wu, X., Abramoff, M., Kardon, R., Sonka, M.: Incorporation of regional information in optimal 3-D graph search with application for intraretinal layer segmentation of optical coherence tomography images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 607–618. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Heimann, T., Munzing, S., Meinzer, H., Wolf, I.: A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 1–12. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Garvin, M.K., Abramoff, M.D., Kardon, R., Russell, S.R., Wu, X., Sonka, M.: Intraretinal layer segmentation of macular optical coherence tomography images using optimal 3-D graph search. IEEE Trans. Med. Imaging 27(10), 1495–1505 (2008)

    Article  Google Scholar 

  8. Yin, Y., Zhang, X., Sonka, M.: Optimal multi-object multi-surface graph search segmentation: Full-joint cartilage delineation in 3D. In: Medical Image Understanding and Analysis 2008, pp. 104–108 (2008)

    Google Scholar 

  9. Li, K., Jolly, M.P.: Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in ct images. In: Proc. SPIE, vol. 6914, pp. 69143S–69143S–11 (2008)

    Google Scholar 

  10. Kainmueller, D., Lamecker, H., Zachow, S., Heller, M., Hege, H.C.: Multi-object segmentation with coupled deformable models. In: Proc. of Medical Image Understanding and Analysis, pp. 34–38 (2008)

    Google Scholar 

  11. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945)

    Article  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Yin, Y., Song, Q., Sonka, M. (2009). Electric Field Theory Motivated Graph Construction for Optimal Medical Image Segmentation. In: Torsello, A., Escolano, F., Brun, L. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2009. Lecture Notes in Computer Science, vol 5534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02124-4_34

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  • DOI: https://doi.org/10.1007/978-3-642-02124-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02123-7

  • Online ISBN: 978-3-642-02124-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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