Efficient Algorithms for the Optimal-Ratio Region Detection Problems in Discrete Geometry with Applications | SpringerLink
Skip to main content

Efficient Algorithms for the Optimal-Ratio Region Detection Problems in Discrete Geometry with Applications

  • Conference paper
Algorithms and Computation (ISAAC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4288))

Included in the following conference series:

Abstract

In this paper, we study several interesting optimal-ratio region detection (ORD) problems in d-D (d ≥3) discrete geometric spaces, which arise in high dimensional medical image segmentation. Given a d-D voxel grid of n cells, two classes of geometric regions that are enclosed by a single or two coupled smooth heightfield surfaces defined on the entire grid domain are considered. The objective functions are normalized by a function of the desired regions, which avoids a bias to produce an overly large or small region resulting from data noise. The normalization functions that we employ are used in real medical image segmentation. To our best knowledge, no previous results on these problems in high dimensions are known. We develop a unified algorithmic framework based on a careful characterization of the intrinsic geometric structures and a nontrivial graph transformation scheme, yielding efficient polynomial time algorithms for solving these ORD problems. Our main ideas include the following. We show that the optimal solution to the ORD problems can be obtained via the construction of a convex hull for a set of O(n) unknown 2-D points using the hand probing technique. The probing oracles are implemented by computing a minimum s-t cut in a weighted directed graph. The ORD problems are then solved by O(n) calls to the minimum s-t cut algorithm. For the class of regions bounded by a single heighfield surface, our further investigation shows that the O(n) calls to the minimum s-t cut algorithm are on a monotone parametric flow network, which enables to detect the optimal-ratio region in the complexity of computing a single maximum flow.

This research was supported in part by an NIH-NIBIB research grant R01-EB004640, in part by a faculty start-up fund from the University of Iowa, and in part by a fund from the American Cancer Society through an Institutional Research Grant to the Holden Comprehensive Cancer Center, the University of Iowa, Iowa City, Iowa, USA.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Amir, A., Kashi, R., Netanyalm, N.S.: Analyzing Quantitative Databases: Image Is Everything. In: Proc. 27th Int. Conf. on Very Large Data Bases, Italy, pp. 89–98 (2001)

    Google Scholar 

  2. Asano, T., Chen, D.Z., Katoh, N., Tokuyama, T.: Efficient Algorithms for Optimization-Based Image Segmentation. Int’l J. of Computational Geometry and Applications 11, 145–166 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  3. Bloch, I.: Apatial Relationship between Objects and Fuzzy Objects using Mathematical Morphology. In: Geometry, Morphology and Computational Imaging, 11th Dagsthul Workshop on Theoretical Foundations of Computer Vision (April 2002)

    Google Scholar 

  4. Chen, D.Z., Chun, J., Katoh, N., Tokuyama, T.: Efficient Algorithms for Approximating a Multi-dimensional Voxel Terrain by a Unimodal Terrain. In: Chwa, K.-Y., Munro, J.I.J. (eds.) COCOON 2004. LNCS, vol. 3106, pp. 238–248. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Chun, J., Sadakane, K., Tokuyama, T.: Linear Time Algorithm for Approximating a Curve by a Single-Peaked Curve. In: Ibaraki, T., Katoh, N., Ono, H. (eds.) ISAAC 2003. LNCS, vol. 2906, pp. 6–15. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Cole, R., Yap, C.K.: Shape from Probing. J. of Algorithms 8, 19–38 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  7. Dobkin, D., Edelsbrunner, H., Yap, C.K.: Probing Convex Polytopes. In: Proc. 18th Annual ACM Symp. on Theory of Computing, pp. 387–392 (1986)

    Google Scholar 

  8. Fukuda, T., Morimoto, Y., Morishita, S., Tokuyama, T.: Data Mining with Optimized Two-Dimensional Association Rules. ACM Transaction on Database Systems 26, 179–213 (2001)

    Article  MATH  Google Scholar 

  9. Gallo, G., Grigoriadis, M.D., Tarjan, R.E.: A Fast parametric maximum flow algorithm and applications. SIAM J. Comput. 18, 30–55 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  10. Goldberg, A.V., Tarjan, R.E.: A New Approach to the Maximum-flow Problem. J. Assoc. Comput. Mach. 35, 921–940 (1988)

    MATH  MathSciNet  Google Scholar 

  11. Gondran, M., Minous, M.: Graphs and Algorithms. John Wiley, New York (1984)

    MATH  Google Scholar 

  12. Gusfield, D., Martel, C.: A Fast Algorithm for the Generalized Parametric Minimum Cut Problem and Applications. Algorithmica 7, 499–519 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  13. Hand, D.J.: Discrimination and Classification. John Wiley & Sons, Chichester (1981)

    MATH  Google Scholar 

  14. Hochbaum, D.S.: A New-old Algorithm for Minimum-cut and Maximum-flow in Closure Graphs. Networks 37(4), 171–193 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  15. Li, K., Wu, X., Chen, D.Z., Sonka, M.: Optimal Surface Segmentation in Volumetric Images – A Graph-Theoretic Approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 28, 119–134 (2006)

    Article  Google Scholar 

  16. Picard, J.C.: Maximal Closure of a Graph and Applications to Combinatorial Problems. Management Science 22, 1268–1272 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  17. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  18. Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 2nd edn. Brooks/Cole Publishing Company, Pacific Grove (1999)

    Google Scholar 

  19. Stahl, J., Wang, S.: Convex Grouping Combining Boundary and Region Information. In: IEEE Int. Conf. on Computer Vision, vol. II, pp. 946–953 (2005)

    Google Scholar 

  20. Wu, X., Chen, D.Z., Li, K., Sonka, M.: The Layered Net Surface Problems in Discrete Geometry and Medical Image Segmentation. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 17–27. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, X. (2006). Efficient Algorithms for the Optimal-Ratio Region Detection Problems in Discrete Geometry with Applications. In: Asano, T. (eds) Algorithms and Computation. ISAAC 2006. Lecture Notes in Computer Science, vol 4288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11940128_30

Download citation

  • DOI: https://doi.org/10.1007/11940128_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49694-6

  • Online ISBN: 978-3-540-49696-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics