Skip to main content

Image Completion Optimised for Realistic Simulations of Wound Development

  • Conference paper
Pattern Recognition (DAGM/OAGM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7476))

  • 3989 Accesses

Abstract

Treatment costs for chronic wound healing disturbances have a strong impact on the health care system. In order to motivate patients and thus reduce treatment times there was the need to visualize possible wound developments based on the current situation of the affected body part. Known disease patterns were used to build a model for simulating the healing as well as the worsening process. The key point for the construction of possible wound stages was the creation of a nicely fitting texture including all representative tissue types. Since wounds are mostly circularly shaped, as first step of the healing an image completion based on radial texture synthesis of small patches from the healthy tissue surrounding the wound was developed. The radial information of the wound border was used to optimize the overlap between individual patches. In a similar way complete layers of all other appearing tissue types were constructed and superimposed using masks representing trained possible appearances. Results show that the developed texture synthesis together with the trained knowledge is perfectly suited to construct realistic wound images for different stages of the disease.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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.

References

  1. Akestes GmbH: WundManager (2001), http://www.akestes.de/ (accessed June 20, 2012)

  2. Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The Generalized PatchMatch Correspondence Algorithm. In: Daniilidis, K. (ed.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  3. Bon, F.X., Briand, E., Guichard, S., Couturaud, B., Revol, M., Servant, J.M., Dubertret, L.: Quantitative and kinetic evolution of wound healing through image analysis. Medical Imaging 19(7), 767–772 (2000)

    Article  Google Scholar 

  4. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2, 27:1–27:27 (2011)

    Google Scholar 

  6. Coloplast GmbH: Wunddokumentation, http://www.coloplast.de/wundversorgung/wundeverstehen/wundmanagement/ (accessed June 20, 2012)

  7. Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. Image Processing 13, 1200–1212 (2004)

    Article  Google Scholar 

  8. Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH, pp. 341–346 (2001)

    Google Scholar 

  9. Jalomed GmbH: JalomedWD, http://www.jalomed.de/de/ (accessed June 20, 2012)

  10. Kolesnik, M., Fexa, A.: Multi-dimensional Color Histograms for Segmentation of Wounds in Images. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 1014–1022. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. Image Processing 16(11), 2649–2661 (2007)

    Article  MathSciNet  Google Scholar 

  12. Lafreniere, D.: An implementation of Komodakis’ and Tziritas’ Image Completion Using Efficient Belief Propagation Via Priority Scheduling and Dynamic Pruning, http://lafarren.com/image-completer/ , (accessed June 20, 2012)

  13. Lashkia, G.V., Anthony, L.: An inductive learning method for medical diagnosis. Pattern Recognition Letters 24(1-3), 273–282 (2003)

    Article  Google Scholar 

  14. Liang, L., Liu, C., Xu, Y., Guo, B., Yeung Shum, H.: Real-time texture synthesis by patch-based sampling. ACM Trans. on Graphics 20, 127–150 (2001)

    Article  Google Scholar 

  15. Mansfield, A., Prasad, M., Rother, C., Sharp, T., Kohli, P., Gool, L.V.: Transforming image completion. In: BMVC, pp. 121.1–121.11 (2011)

    Google Scholar 

  16. Medizinische Universität Wien: W.H.A.T. (Wound Healing Analysing Tool), http://cemsiis.meduniwien.ac.at/mbm/wf/projekte/what/ (accessed June 20, 2012)

  17. Nock, R., Nielsen, F.: Statistical region merging. PAMI 26, 1452–1458 (2004)

    Article  Google Scholar 

  18. Ojala, T., Pietikaeinen, M., Maeenpaeae, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–987 (2002)

    Article  Google Scholar 

  19. Ojala, T., Pietikäinen, M., Mäenpää, T.: A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification. In: Singh, S., Murshed, N., Kropatsch, W.G. (eds.) ICAPR 2001. LNCS, vol. 2013, pp. 397–406. Springer, Heidelberg (2001)

    Google Scholar 

  20. Seevinck, J., Scerbo, M.W., Belfore, L.A., Weireter, L.J., Crouch, J.R., Shen, Y., McKenzie, F.D., Garcia, H.M., Girtelschmid, S., Baydogan, E., Schmidt, E.A.: A Simulation-Based Training System for Surgical Wound Debridement. Studies in Health Technology and Informatics 119, 491–496 (2006)

    Google Scholar 

  21. Treuillet, S., Albouy, B., Lucas, Y.: Three-dimensional assessment of skin wounds using a standard digital camera. Medical Imgaging 28(5), 752–762 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Schneeberger, M., Uray, M., Mayer, H. (2012). Image Completion Optimised for Realistic Simulations of Wound Development. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds) Pattern Recognition. DAGM/OAGM 2012. Lecture Notes in Computer Science, vol 7476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32717-9_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32717-9_45

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32716-2

  • Online ISBN: 978-3-642-32717-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics