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Fusion of Multi-view Tissue Classification Based on Wound 3D Model

  • Hazem Wannous
  • Yves Lucas
  • Sylvie Treuillet
  • Benjamin Albouy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)

Abstract

Region classification from a single image is no more reliable when the labeling must be applied on a 3D surface. Depending on camera viewpoint and surface curvature, lighting variations and perspective effects alter colorimetric analysis and area measurements. This problem can be overcome if a 3D model of the object of interest is available. This general approach has been evaluated for the design of a complete wound assessment tool using a simple free handled digital camera. Clinical tests demonstrate that multi view classification results in enhanced tissue labeling and more precise measurements, a significant step toward accurate monitoring of the healing process.

Keywords

Fusion Algorithm Stereo Pair Dominant Class Tissue Class Tissue Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Keast, D.H., et al.: MEASURE: A proposed assessment framework for developing best practice recommendations for wound assessment. Wound Repair and Regeneration 12, s1–s17 (2004)Google Scholar
  2. 2.
    Ozturk, C., Dubin, S., Schafer, M.E., Shi, W.Y., Chou, M.C.: A new structured light method for 3-D wound measurement. In: Proc. of the IEEE Annual Northeast Bioeng. Conf., New Brunswick, NJ, USA, March 14-15, pp. 70–71 (1996)Google Scholar
  3. 3.
    Krouskop, T.A., Baker, R., Wilson, M.S.: A noncontact wound measurement system. Journal of Rehabilitation Research and Development 39(3), 337–346 (2002)Google Scholar
  4. 4.
    Lubeley, D., Jostschulte, K., Kays, R., Biskup, K., Clasbrummel, B.: 3D Wound measurement system for telemedical applications. Biomedizinische Technik 50, 1418–1419 (2005)Google Scholar
  5. 5.
    Boersma, S.M., Van den Heuvel, F.A., Cohen, A.F., Scholtens, R.E.M.: Photogrammetric wound measurement with a three-camera vision system. In: Int. Archives of Photogrammetry and Remote Sensing, Amsterdam, vol. XXXIII (2000)Google Scholar
  6. 6.
    Malian, A., Azizi, A., Heuvel Van Den, F.A., Zolfaghari, M.: Development of a robust photogrammetric metrology system for monitoring the healing of bedsores. Photogrammetric Record 20(111), 241–273 (2005)CrossRefGoogle Scholar
  7. 7.
    Plassman, P., Jones, T.D.: MAVIS: a non-invasive instrument to measure area and volume of wounds. Med. Eng. Phys. 20(5), 332–338 (1998)CrossRefGoogle Scholar
  8. 8.
    Romanelli, M., Gaggio, G., Piaggesi, A., Coluccia, M., Rizello, F.: Technological advances in wound bed Measurements. Wounds 14(2), 58–66 (2002)Google Scholar
  9. 9.
    Callieri, M., Cignoni, P., Coluccia, M., Gaggio, G., Pingi, P., Romanelli, M., Scopigno, R.: Derma: monitoring the evolution of skin lesions with a 3D system. In: 8th Int. Workshop on Vision, Modeling and Visualization, Munich, Novomber 19-21, pp. 167–174 (2003)Google Scholar
  10. 10.
    Liu, X., Kim, W., Schmidt, R., Drerup, B., Song, J.: Wound measurement by curvature maps: a feasibility study. Physiol. Meas. 27, 1107–1123 (2006)CrossRefGoogle Scholar
  11. 11.
    MAVIS II: 3D Wound instrument measurement, University of Glamorgan (2006), http://imaging.research.glam.ac.uk/projects/wm/mavis/
  12. 12.
    Duckworth, M., Patel, N., Joshi, A., Lankton, S.: A clinically affordable non-contact wound measurement device. In: Proceedings of 30th RESNA conference on technology and disability, Phoenix, USA, June 15-19 (2007)Google Scholar
  13. 13.
    Oduncu, H., Hoppe, A., Clark, M., Williams, R.J., Harding, K.G.: Analysis of skin wound images using digital color image processing: a preliminary communication. Lower Extremity Wounds 3(3), 151–156 (2004)CrossRefGoogle Scholar
  14. 14.
    Perez, A., Gonzaga, A., Alves, J.: Segmentation and analysis of leg ulcers color images. Medical Imaging and Augmented Reality, Hong Kong, June 10-12, pp. 262–266 (2001)Google Scholar
  15. 15.
    Zheng, H., Bradley, L., Patterson, D., Galushka, M.: New protocol for leg ulcer tissue classification from color images. In: IEEE EMBS, San Francisco, vol. 2, pp. 1389–1392 (2004)Google Scholar
  16. 16.
    Kolesnik, M., Fexa, A.: Segmentation of Wounds in the Combined Color-Texture Feature Space. Medical Imaging 5370, 549–556 (2004)Google Scholar
  17. 17.
    Galushka, M., Zheng, H., Patterson, D., Bradley, L.: Case-based tissue classification for monitoring leg ulcer healing. In: CBMS, pp. 353–358 (2005)Google Scholar
  18. 18.
    Lucas, Y., Treuillet, S., Albouy, B., Wannous, H., Pichaud, J.C.: 3D and color wound assessment using a simple digital camera. In: 9th Meeting of the European Pressure Ulcer Advisory Panel, Berlin (September 2006)Google Scholar
  19. 19.
    Albouy, B., Koenig, E., Treuillet, S., Lucas, Y.: Accurate 3D Structure Measurements from Two Uncalibrated Views. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2006. LNCS, vol. 4179, pp. 1111–1121. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  20. 20.
    Wannous, H., Treuillet, S., Lucas, Y.: Supervised Tissue Classification from Color Images for a Complete wound Assessment tool. In: 29th Conf. of IEEE Engineering in Medecine and Biology Society, Lyon, France, August 23-26 (2007)Google Scholar
  21. 21.
    Wannous, H., Lucas, Y., Treuillet, S.: Efficient SVMs classifier based on color and texture region features for wound tissue images. In: SPIE Medical imaging, San Diego, USA, February 16-21, SPIE Digital Library. Proc. SPIE, vol. 6915, 69152T (2008)Google Scholar
  22. 22.
    Albouy, B., Lucas, Y., Treuillet, S.: Volumetric assessment of skin wound using a free handled digital camera. In: 29th Conf. of IEEE Engineering in Medecine and Biology Society, Lyon, France, August 23-26 (2007)Google Scholar
  23. 23.
    Deng, Y., Manjunath, B.S.: Unsupervised segmentation of color textures regions in images and video. IEEE Trans. on PAMI 23(8), 140–147 (2001)CrossRefGoogle Scholar
  24. 24.
    Comaniciu, D., Meer, P.: Robust analysis of feature space: Color image segmentation. In: Proceedings IEEE Conf. on CVPR, Puerto Rico, pp. 750–755 (1997)Google Scholar
  25. 25.
    Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)CrossRefGoogle Scholar
  26. 26.
    Joachims, T.: Making Large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  27. 27.
    Tian, G.Y., Gledhill, D., Taylor, D., Clarke, D.: Color Correction for Panoramic Imaging. In: IV 2002, pp. 483–488 (2002)Google Scholar
  28. 28.
    Albouy, B., Treuillet, S., Lucas, Y.: Finding Two Optimal Positions of a Hand-Held Camera for the Best 3D Reconstruction. In: 3DTV Conference, Kos Island, Greece, May 7-9 (2007)Google Scholar
  29. 29.
    Hartley, R.I., Zisserman, A.: Multiple View geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  30. 30.
    Albouy, B., Treuillet, S., Lucas, Y.: Robust semi-dense matching across uncalibrated and widely separated views. In: MVA 2007 IAPR Conference on machine vision applications, University of Tokyo, Japan, May 16-18 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Hazem Wannous
    • 1
  • Yves Lucas
    • 2
  • Sylvie Treuillet
    • 3
  • Benjamin Albouy
    • 4
  1. 1.Institut PRISME, ENSI de BourgesBourgesFrance
  2. 2.Institut PRISMEIUT Bourges Université d’Orléans av. de LattreBourgesFrance
  3. 3.Institut PRISMEEcole Polytechnique Université d’OrléansOrléansFrance
  4. 4.LAICIUT Le Puy en Velay, Université Clermont ILe Puy en VelayFrance

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