Spatial Accuracy of a Low Cost High Resolution 3D Surface Imaging Device for Medical Applications


The Kinect is a low-cost motion-sensing device designed for Microsoft’s Xbox 360. Software has been created that enables user to access data from the Kinect, enhancing its versatility. This study characterizes the spatial accuracy and precision of the Kinect for creating 3D images for use in medical applications. Measurements of distances between surface features on both flat and curved objects were made using 3D images created by the Kinect. These measurements were compared to control measurements made by a ruler, calipers or by a CT scan and using the ruler tools provided. Measurements on flat surfaces matched closely to control measurements, with average differences between the Kinect and control measurements of less than 2 mm and percent errors of less than 1%. Measurements on curved surfaces also matched control measurements but errors up to 3mm occurred when measuring protruding surface features or features along lateral boundaries of objects. The Kinect is an alternative to other 3D imaging devices such as CT scanners, laser scanners and photogrammetric devices. Alternative 3D meshing algorithms and combining images from multiple Kinects could resolve errors made when using the Kinect to measure features on curved surfaces. Medical applications include craniofacial anthropometry, radiotherapy patient positioning and surgical planning.

Share and Cite:

B. Shin, R. Venkatramani, P. Borker, A. Olch, J. Grimm and K. Wong, "Spatial Accuracy of a Low Cost High Resolution 3D Surface Imaging Device for Medical Applications," International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol. 2 No. 2, 2013, pp. 45-51. doi: 10.4236/ijmpcero.2013.22007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] S. Winkelbach, “David 3D Scanner,” 2011.
[2] S. B. Sholts, S. K. T. S. Wärmländer, L. M. Flores, K. W. P. Miller and P. L. Walker, “Variation in the Measurement of Cranial Volume and Surface Area Using 3D Laser Scanning Technology,” Journal of Forensic Sciences, Vol. 55, No. 4, 2010, pp. 871-876. doi:10.1111/j.1556-4029.2010.01380.x
[3] J. Y. Wong, A. K. Oh, E. Ohta, A. T. Hunt, G. F. Rogers J. B. Mulliken and C. K. Deutsch, “Validity and Reliability of Craniofacial Anthropometric Measurement of 3D Digital Photogrammetric Images,” Cleft Palate Craniofacial Journal, Vol. 45, No. 3, 2008, pp. 232-239. doi:10.1597/06-175
[4] C. H. Kau, S. Richmond, A. Incrapera, J. English and J. J. Xia, “Three-Dimensional Surface Acquisition Systems for the Study of Facial Morphology and Their Application to Maxillofacial Surgery,” The International Journal of Medical Robotics and Computer Assisted Surgery, Vol. 3, No. 2, 2007, pp. 97-110. doi:10.1002/rcs.141
[5] A. Losken, H. Seify, D. D. Denson, A. A. Paredes Jr. and G. W. Carlson. “Validating Three-Dimensional Imaging of the Breast,” Annals of Plastic Surgery, Vol. 54, No. 5, 2005, pp. 471-476. doi:10.1097/
[6] K. Aldridge, S. A. Boyadijev, G. T. Capone, V. B. DeLeon and J. T. Richtsmeier, “Precision and Error of Three-Dimensional Phenotypic Measures Acquired from 3dMD Photogrammetric Images,” American Journal of Medical Genetics Part A, Vol. 138A, No. 3, 2005, pp. 247-253. doi:10.1002/ajmg.a.30959
[7] M. Krimmel, S. Kluba, M. Bacher, K. Dietz and S. Reinert, “Digital Surface Photogrammetry for Anthropometric Analysis of the Cleft Infant Face,” Cleft Palate Craniofacial Journal, Vol. 43, No. 3, 2006, pp. 350-355.
[8] F. Menna, F. Remondino, R. Battisti and E. Nocerino. “Geometric Investigation of a Gaming Active Device,” Proceedings SPIE, Vol. 8085, 2011, p. 15. doi:10.1117/12.890070
[9] A. Shpunt and Z. Zalevsky, “Three-Dimensional Sensing Using Speckle Patterns,” U.S. Patent No. 0096783, 2009.
[10] “OpenNI,” 2011.
[11] “NITE Middleware, PrimeSense Natural Interaction,” 2011.
[12] N. Burrus, “Kinect RGBDemo v0.5.0, Nicolas Burrus Homepage,” 2011.
[13] “MeshLab,” 2011.
[14] A. C. Öztireli, G. Guennebaud and M. Gross, “Feature Pre-Serving Point Set Surfaces Based on Non-Linear Kernel Regression,” Computer Graphics Forum, Vol. 28, No. 2, 2009, pp. 493-501. doi:10.1111/j.1467-8659.2009.01388.x
[15] M. Kazhdan, M. Bolitho and H. Hoppe, “Poisson Surface Reconstruction,” Proceedings of the 4th Eurographics Symposium on Geometry Processing, Cagliari, 26-28 June 2006, pp. 61-70.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.