Dictionary Based Segmentation in Volumes

  • Monica Jane EmersonEmail author
  • Kristine Munk Jespersen
  • Peter Stanley Jørgensen
  • Rasmus Larsen
  • Anders Bjorholm Dahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)


We present a method for supervised volumetric segmentation based on a dictionary of small cubes composed of pairs of intensity and label cubes. Intensity cubes are small image volumes where each voxel contains an image intensity. Label cubes are volumes with voxel-wise probabilities for a given label. The segmentation process is done by matching a cube from the volume, of the same size as the dictionary intensity cubes, to the most similar intensity dictionary cube, and from the associated label cube we get voxel-wise label probabilities. Probabilities from overlapping cubes are averaged and hereby we obtain a robust label probability encoding. The dictionary is computed from labeled volumetric image data based on weighted clustering. We experimentally demonstrate our method using two data sets from material science – a phantom data set of a solid oxide fuel cell simulation for detecting three phases and their interfaces, and a tomogram of a glass fiber composite used in wind turbine blades for detecting individual glass fibers.


Volume segmentation Materials images X-ray tomography Learning dictionaries Glass fiber segmentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aldasoro, C.C.R., Bhalerao, A.: Volumetric texture segmentation by discriminant feature selection and multiresolution classification. IEEE TMI 26(1), 1–14 (2007)Google Scholar
  2. 2.
    Blot, L., Zwiggelaar, R.: Synthesis and analysis of solid texture: application in medical imaging, pp. 9–12 (2002)Google Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72(2), 195–215 (2007)CrossRefGoogle Scholar
  5. 5.
    Dahl, A.L., Larsen, R.: Learning dictionaries of discriminative image patches. In: 22nd BMVC (2011)Google Scholar
  6. 6.
    Government, T.D.: The danish climate policy plan - towards a low carbon society. Technical report, Dahish Energy Agency (2013)Google Scholar
  7. 7.
    Hansen, J.Z., Brøndsted, P., Jacobsen, T.K.: The effects of fibre architecture on fatigue life-time of composite materials. Ph.D. thesis, Technical University of Denmark, Risø National Laboratory for Sustainable Energy (2013)Google Scholar
  8. 8.
    Ilea, D.E., Whelan, P.F.: Image segmentation based on the integration of colour-texture descriptorsa review. Pattern Recognition 44(10), 2479–2501 (2011)zbMATHCrossRefGoogle Scholar
  9. 9.
    Jørgensen, P.S., Yakal-Kremski, K., Wilson, J., Bowen, J.R., Barnett, S.: On the accuracy of triple phase boundary lengths calculated from tomographic image data. Journal of Power Sources 261, 198–205 (2014)CrossRefGoogle Scholar
  10. 10.
    Jørgensen, P., Ebbehøj, S., Hauch, A.: Triple phase boundary specific pathway analysis for quantitative characterization of solid oxide cell electrode microstructure. Journal of Power Sources 279, 686–693 (2015)CrossRefGoogle Scholar
  11. 11.
    Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 147–159 (2004)CrossRefGoogle Scholar
  12. 12.
    Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 79–116 (1998)CrossRefGoogle Scholar
  13. 13.
    Nijssen, R.P.L.: Fatigue life prediction and strength degradation of wind turbine rotor blade composites. Contractor Report SAND2006-7810P, Sandia National Laboratories, Albuquerque, NM (2006)Google Scholar
  14. 14.
    Randen, T., Monsen, E., Signer, C., Abrahamsen, A., Hansen, J.O., Sæter, T., Schlaf, J., Sønneland, L., et al.: Three-dimensional texture attributes for seismic data analysis. In: 70th Annual International Meeting, Society of Exploration Geophysics Expanded Abstracts, pp. 668–671 (2000)Google Scholar
  15. 15.
    Tai, C., Baba-Kishi, K.: Microtexture studies of pst and pzt ceramics and pzt thin film by electron backscatter diffraction patterns. Textures and Microstructures 35(2), 71–86 (2002)CrossRefGoogle Scholar
  16. 16.
    Tu, Z., Zhou, X.S., Comaniciu, D., Bogoni, L.: A learning based approach for 3D segmentation and colon detagging. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 436–448. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  17. 17.
    Ushizima, D., Parkinson, D., Nico, P., Ajo-Franklin, J., MacDowell, A., Kocar, B., Bethel, W., Sethian, J.: Statistical segmentation and porosity quantification of 3d x-ray microtomography. In: SPIE Optical Engineering Applications, pp. 813502–813502. International Society for Optics and Photonics (2011)Google Scholar
  18. 18.
    Waggoner, J., Zhou, Y., Simmons, J., De Graef, M., Wang, S.: 3d materials image segmentation by 2d propagation: a graph-cut approach considering homomorphism. IEEE TIP 22(12), 5282–5293 (2013)Google Scholar
  19. 19.
    Zhang, J., Tan, T.: Brief review of invariant texture analysis methods. Pattern recognition 35(3), 735–747 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Monica Jane Emerson
    • 1
    Email author
  • Kristine Munk Jespersen
    • 2
  • Peter Stanley Jørgensen
    • 3
  • Rasmus Larsen
    • 1
  • Anders Bjorholm Dahl
    • 1
  1. 1.DTU ComputeLyngbyDenmark
  2. 2.DTU Wind EnergyRoskildeDenmark
  3. 3.DTU EnergyRoskildeDenmark

Personalised recommendations