Threshold of Graph-Based Volumetric Segmentation

  • Dumitru Dan Burdescu
  • Liana Stanescu
  • Marius Brezovan
  • Cosmin Stoica Spahiu
  • Florin Slabu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. Volumetric image segmentation can simply result an image partition composed by relevant regions, but the most fundamental challenge in segmentation algorithm is to precisely define the spatial extent of some object, which may be represented by the union of multiple regions. The aim in this paper is to present a new and efficient method as complexity to detect visual objects from color volumetric images and efficient threshold. We present a unified framework for original volumetric segmentation that uses a tree-hexagonal structure defined on the set of the voxels. The advantage of using a tree-hexagonal network superposed over the initial image voxels is that it reduces the execution time and the memory space used, without losing the initial resolution of the image.


Volumetric segmentation Graph-based segmentation Dissimilarity Threshold 


  1. 1.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Allène, C., Audibert, J.-Y., Couprie, M., Keriven, R.: Some links between extremum spanning forests, watersheds and min-cuts. Image Vis. Comput. 8(10), 1460–1471 (2010)CrossRefGoogle Scholar
  3. 3.
    Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient hierarchical graph-based video segmentation. In: Proceedings of IEEE Computer Vision and Pattern Recognition (CVPR), pp. 2141–2148 (2010)Google Scholar
  4. 4.
    Weinlanda, D., Ronfardb, R., Boyerc, E.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224–241 (2011)CrossRefGoogle Scholar
  5. 5.
    Felzenszwalb, P., Huttenlocher, W.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  6. 6.
    Guigues, L., Herve, L., Cocquerez, L.P.: The hierarchy of the cocoons of a graph and its application to image segmentation. Pattern Recogn. Lett. 24(8), 1059–1066 (2003)MATHCrossRefGoogle Scholar
  7. 7.
    Gdalyahu, Y., Weinshall, D., Werman, M.: Self-organization in vision: stochastic clustering for image segmentation, perceptual grouping, and image database organization. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1053–1074 (2001)CrossRefGoogle Scholar
  8. 8.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 885–905 (2000)Google Scholar
  9. 9.
    Jermyn, I., Ishikawa, H.: Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Trans. Pattern Anal. Mach. Intell. 23(8), 1075–1088 (2001)CrossRefGoogle Scholar
  10. 10.
    Cooper, M.: The tractibility of segmentation and scene analysis. Int. J. Comput. Vis. 30(1), 27–42 (1998)CrossRefGoogle Scholar
  11. 11.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. Int. J. Comput. Vis. 43(1), 7–27 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Comaniciu, D., Meer, P.: Robust analysis of feature spaces: color image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  13. 13.
    Stanescu, L., Burdescu, D., Brezovan, M.: A comparative study of some methods for color medical images segmentation. EURASIP J. Adv. Signal Process. 128(1), 5–23 (2011)Google Scholar
  14. 14.
    Brezovan, M., Burdescu, D., Ganea, E., Stanescu, L.: An adaptive method for efficient detection of salient visual object from color images. In: Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 2346–2349 (2010)Google Scholar
  15. 15.
    Burdescu, D., Brezovan, M., Ganea, E., Stanescu, L.: A new method for segmentation of images represented in a HSV color space. In: Fitzgibbon, J., Blanc-Talon, S., Philips, D., Sato, Y., Popescu, C., Scheunders, P. (eds.) Advanced Concepts for Intelligent Vision Systems. LNCS, vol. 5807, pp. 606–760. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Stanescu, L., Burdescu, D., Brezovan, M., Mihai, C.R.G.: Creating New Medical Ontologies for Image Annotation. Springer, New York (2011). ISBN 13: 9781461419082, 10: 1461419085 Google Scholar
  17. 17.
    Burdescu, D., Stanescu, L., Brezovan, M., StoicaSpahiu, C.: Computational complexity analysis of the graph extraction algorithm for 3D segmentation. In: IEEE Tenth World Congress on Services-SERVICES 2014, pp. 462–470 (2014). ISBN-13: 978-1-4799-5069-0Google Scholar
  18. 18.
    Burdescu, D.D., Brezovan, M., Stanescu, L., Stoica-Spahiu, C.: A spatial segmentation method. Int. J. Comput. Sci. Appl. 11(1), 75–100 (2014). ©Technomathematics Research FoundationGoogle Scholar
  19. 19.
    Cormen, T., Leiserson, C., Rivest, R.: Introduction to Algorithms. MIT Press, Cambridge (1990)MATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dumitru Dan Burdescu
    • 1
  • Liana Stanescu
    • 1
  • Marius Brezovan
    • 1
  • Cosmin Stoica Spahiu
    • 1
  • Florin Slabu
    • 2
  1. 1.Computers and Information Technology Department, Faculty of Automatics, Computers and ElectronicsUniversity of CraiovaCraiovaRomania
  2. 2.Departament of Computer ScienceUniversity of CraiovaCraiovaRomania

Personalised recommendations