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.
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References
Akestes GmbH: WundManager (2001), http://www.akestes.de/ (accessed June 20, 2012)
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)
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)
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)
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)
Coloplast GmbH: Wunddokumentation, http://www.coloplast.de/wundversorgung/wundeverstehen/wundmanagement/ (accessed June 20, 2012)
Criminisi, A., Pérez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. Image Processing 13, 1200–1212 (2004)
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH, pp. 341–346 (2001)
Jalomed GmbH: JalomedWD, http://www.jalomed.de/de/ (accessed June 20, 2012)
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)
Komodakis, N., Tziritas, G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. Image Processing 16(11), 2649–2661 (2007)
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)
Lashkia, G.V., Anthony, L.: An inductive learning method for medical diagnosis. Pattern Recognition Letters 24(1-3), 273–282 (2003)
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)
Mansfield, A., Prasad, M., Rother, C., Sharp, T., Kohli, P., Gool, L.V.: Transforming image completion. In: BMVC, pp. 121.1–121.11 (2011)
Medizinische Universität Wien: W.H.A.T. (Wound Healing Analysing Tool), http://cemsiis.meduniwien.ac.at/mbm/wf/projekte/what/ (accessed June 20, 2012)
Nock, R., Nielsen, F.: Statistical region merging. PAMI 26, 1452–1458 (2004)
Ojala, T., Pietikaeinen, M., Maeenpaeae, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. PAMI 24(7), 971–987 (2002)
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)
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)
Treuillet, S., Albouy, B., Lucas, Y.: Three-dimensional assessment of skin wounds using a standard digital camera. Medical Imgaging 28(5), 752–762 (2009)
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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
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DOI: https://doi.org/10.1007/978-3-642-32717-9_45
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