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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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)
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)
Gethin, G., Cowman, S.: Wound measurement comparing the use of acetate tracings and visitraktm digital planimetry. J. Clin. Nurs. 15(4), 422–427 (2006)
Gilman, T.: Wound outcomes: the utility of surface measures. Int. J. Low. Extrem. Wounds 3(3), 125–132 (2004)
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)
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)
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)
Chang, A.C., Dearman, B., Greenwood, J.E.: A comparison of wound area measurement techniques: visitrak versus photography. Eplasty 11(e18), 158–166 (2011)
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)
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)
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)
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)
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)
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)
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)
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))
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)
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)
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)
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)
Whelan, T.: Icpcuda (2015). Accessed 28 March 2015
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)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. SIGGRAPH Comput. Graph. 21(4), 163–169 (1987)
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)
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)
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)
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)
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)
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)
VTK: The visualization toolkit version 6.3 (2015). Accessed 14 Oct 2015
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)
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the Josip Juraj Strossmayer University of Osijek, under Grant No. IZIP-2014-70.
Rights and permissions
About this article
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
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-018-0920-4