Skip to main content
SpringerLink
Log in

Wound measurement by RGB-D camera

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  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)

  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)

  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)

    Article  Google Scholar 

  4. Gilman, T.: Wound outcomes: the utility of surface measures. Int. J. Low. Extrem. Wounds 3(3), 125–132 (2004)

    Article  Google Scholar 

  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)

  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. 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)

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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))

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  21. Whelan, T.: Icpcuda (2015). Accessed 28 March 2015

  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)

  23. Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. VTK: The visualization toolkit version 6.3 (2015). Accessed 14 Oct 2015

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000, Osijek, Croatia

    Damir Filko, Robert Cupec & Emmanuel Karlo Nyarko

Authors
  1. Damir Filko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robert Cupec
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emmanuel Karlo Nyarko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Damir Filko.

Additional information

This work was supported by the Josip Juraj Strossmayer University of Osijek, under Grant No. IZIP-2014-70.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Filko, D., Cupec, R. & Nyarko, E.K. Wound measurement by RGB-D camera. Machine Vision and Applications 29, 633–654 (2018). https://doi.org/10.1007/s00138-018-0920-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0920-4

Keywords

Search

Navigation