Advertisement

Machine Vision and Applications

, Volume 29, Issue 4, pp 633–654 | Cite as

Wound measurement by RGB-D camera

  • Damir Filko
  • Robert Cupec
  • Emmanuel Karlo Nyarko
Original Paper
  • 186 Downloads

Abstract

The robot and computer vision community has seen a lot of novelties developed in the past few years as a result of the appearance of cheap RGB-D sensors spearheaded by the Kinect sensor. In this paper, the feasibility of using an RGB-D camera in detecting, segmenting, reconstructing and measuring chronic wounds in 3D is explored. The wound is detected by implementing nearest-neighbor approach on color histograms generated from the image. The proposed wound segmentation procedure extracts the wound contour using visual and geometrical information of the surface. A procedure comparable to KinectFusion is used for the 3D reconstruction of the wound. In order to achieve real-time performance, the whole system is realized in CUDA. The resulting system provides an accurate colored 3D model of the segmented wound and enables the user to determine the volume, area and perimeter of the wound, thereby aiding in the selection of a suitable therapy. The developed system is experimentally evaluated using the Saymour II wound care model by VATA Inc.

Keywords

Chronic wound Detection 3D reconstruction Segmentation Measurement RGB-D camera 

References

  1. 1.
    Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: Proceedings of the 10th IEEE International Symposium on Mixed and Augmented Reality, 2011, ISMAR ’11, Washington, DC, USA, pp. 127–136. IEEE Computer Society (2011)Google Scholar
  2. 2.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, 2001, pp. 145–152. IEEE (2001)Google Scholar
  3. 3.
    Gethin, G., Cowman, S.: Wound measurement comparing the use of acetate tracings and visitraktm digital planimetry. J. Clin. Nurs. 15(4), 422–427 (2006)CrossRefGoogle Scholar
  4. 4.
    Gilman, T.: Wound outcomes: the utility of surface measures. Int. J. Low. Extrem. Wounds 3(3), 125–132 (2004)CrossRefGoogle Scholar
  5. 5.
    Filko, D., Antonic, D., Huljev, D.: Wita—application for wound analysis and management. In: 12th International Conference on e-Health Networking Applications and Services (Healthcom), 2010, pp. 68–73. IEEE (2010)Google Scholar
  6. 6.
    Mukherjee, R., Manohar, D.D., Das, D.K., Achar, A., Mitra, A., Chakraborty, C.: Automated tissue classification framework for reproducible chronic wound assessment. BioMed Res. Int. 2014, 1–9 (2014)Google Scholar
  7. 7.
    Wang, C., Yan, X., Smith, M., Kochhar, K., Rubin, M., Warren, S.M., Wrobel, J., Lee, H.: A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. In: 37th Annual International Conference on IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 2415–2418. IEEE (2015)Google Scholar
  8. 8.
    Chang, A.C., Dearman, B., Greenwood, J.E.: A comparison of wound area measurement techniques: visitrak versus photography. Eplasty 11(e18), 158–166 (2011)Google Scholar
  9. 9.
    Treuillet, S., Albouy, B., Lucas, Y.: Three-dimensional assessment of skin wounds using a standard digital camera. IEEE Trans. Med. Imaging 28(5), 752–762 (2009)CrossRefGoogle Scholar
  10. 10.
    Bowling, F.L., King, L., Paterson, J.A., Hu, J., Lipsky, B.A., Matthews, D.R., Boulton, A.J.: Remote assessment of diabetic foot ulcers using a novel wound imaging system: remote foot ulcer assessment using a wound imaging system. Wound Repair Regen. 19(1), 25–30 (2011)CrossRefGoogle Scholar
  11. 11.
    Callieri, M., Cignoni, P., Pingi, P., Scopigno, R., Coluccia, M., Gaggio, G., Romanelli, M.N.: Derma: monitoring the evolution of skin lesions with a 3D system. In: VMV, pp. 167–174 (2003)Google Scholar
  12. 12.
    Zvietcovich, F., Castaeda, B., Valencia, B., Llanos-Cuentas, A.: A 3D assessment tool for accurate volume measurement for monitoring the evolution of cutaneous leishmaniasis wounds. In: Annual International Conference on Engineering in Medicine and Biology Society (EMBC), 2012, pp. 2025–2028. IEEE (2012)Google Scholar
  13. 13.
    Pavlovcic, U., Diaci, J., Mozina, J., Jezersek, M.: Wound perimeter, area, and volume measurement based on laser 3D and color acquisition. BioMedical Eng. OnLine 14(1), 39 (2015)CrossRefGoogle Scholar
  14. 14.
    Bills, J.D., Berriman, S.J., Noble, D.L., Lavery, L.A., Davis, K.E.: Pilot study to evaluate a novel three-dimensional wound measurement device: three-dimensional wound assessment tool. Int. Wound J. 13(6), 1372–1377 (2016)CrossRefGoogle Scholar
  15. 15.
    Wu, K., Amling, J., Howell, A., Kim, P., Guler, O.: Mobile structure sensor for real-time 3D wound assessment: ex-vivo validation using wound phantoms. In: 47th Annual Conference of Wound, Ostomy and Continence Nurses Society, WOCN (2015)Google Scholar
  16. 16.
    Filko, D., Cupec, R., Nyarko, E.K.: Detection, reconstruction and segmentation of chronic wounds using Kinect v2 sensor. Proc. Comput. Sci. 90, 151–156 (2016). (20th Conference on Medical Image Understanding and Analysis (MIUA 2016))CrossRefGoogle Scholar
  17. 17.
    Filko, D., Nyarko, E.K., Cupec, R.: Wound detection and reconstruction using RGB-D camera. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016, pp. 1217–1222 (2016)Google Scholar
  18. 18.
    Lachat, E., Macher, H., Mittet, M., Landes, T., Grussenmeyer, P.: First experiences with Kinect v2 sensor for close range 3D modelling. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 40(5), 93 (2015)CrossRefGoogle Scholar
  19. 19.
    Zhang, C., Zhang, Z.: Calibration between depth and color sensors for commodity depth cameras. In: IEEE International Conference on Multimedia and Expo, 2011, pp. 1–6 (2011)Google Scholar
  20. 20.
    Herrera, D.C., Kannala, J., Heikkil, J.: Joint depth and color camera calibration with distortion correction. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 2058–2064 (2012)CrossRefGoogle Scholar
  21. 21.
    Whelan, T.: Icpcuda (2015). Accessed 28 March 2015Google Scholar
  22. 22.
    Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’96, New York, NY, USA, pp. 303–312. ACM (1996)Google Scholar
  23. 23.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)CrossRefGoogle Scholar
  24. 24.
    Susstrunk, S., Fua, P., Shaji, A., Lucchi, A., Smith, K., Achanta, R.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)CrossRefGoogle Scholar
  25. 25.
    Yang, J., Gan, Z., Li, K., Hou, C.: Graph-based segmentation for rgb-d data using 3-D geometry enhanced superpixels. IEEE Trans. Cybern. 45(5), 927–940 (2015)CrossRefGoogle Scholar
  26. 26.
    Papon, J., Abramov, A., Schoeler, M., Worgotter, F.: Voxel cloud connectivity segmentation—supervoxels for point clouds. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’13, Washington, DC, USA, pp. 2027–2034. IEEE Computer Society (2013)Google Scholar
  27. 27.
    Stuckler, J., Behnke, S.: Multi-resolution surfel maps for efficient dense 3D modeling and tracking. J. Vis. Commun. Image Represent. 25(1), 137–147 (2014)CrossRefGoogle Scholar
  28. 28.
    Holz, D., Behnke, S.: Approximate triangulation and region growing for efficient segmentation and smoothing of range images. Robot. Auton. Syst. 62(9), 1282–1293 (2014)CrossRefGoogle Scholar
  29. 29.
    Rabbani, T., Van Den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(5), 248–253 (2006)Google Scholar
  30. 30.
    VTK: The visualization toolkit version 6.3 (2015). Accessed 14 Oct 2015Google Scholar
  31. 31.
    Alyassin, A.M., Lancaster, J.L., Downs, J.H., Fox, P.T.: Evaluation of new algorithms for the interactive measurement of surface area and volume. Med. Phys. 21(6), 741–752 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Damir Filko
    • 1
  • Robert Cupec
    • 1
  • Emmanuel Karlo Nyarko
    • 1
  1. 1.Faculty of Electrical Engineering, Computer Science and Information Technology OsijekJosip Juraj Strossmayer University of OsijekOsijekCroatia

Personalised recommendations