Mesh Smoothing of 3D Prosthesis and Orthosis Models
https://doi.org/10.32603/1993-8985-2024-27-5-108-117
Abstract
Introduction. Formation of a contact surface in prosthetics and orthotics is crucial for the restoration of human musculoskeletal functions. This paper considers specific features of methods currently used for denoising of a 3D model surface obtained by optical scanning. An algorithm for manufacturing an individual prosthetic and orthopedic product is developed. The current literature reports no similar methods, which may be explained by the widespread use of gypsum technology for the manufacture of prostheses and orthoses.
Aim. Research and development of digital filtration methods for 3D meshes obtained by optical scanning for further modeling of individual prosthesis and orthosis modules.
Materials and methods. It is proposed to optimize the pre-processing stage of 3D scanned models by applying denoising and smoothing processes. In total, 50 optical 3D scans were selected for testing via the following denoising algorithms: bilateral filtering, vertex-based anisotropic smoothing, mean and median filtering applied to face normals.
Results. The study was conducted using 3D scans of lower limb stumps and Chenault brace orthoses and corsets provided by the Institute of Prosthetics and Orthotics, Albrecht Federal Scientific and Educational Centre of Medical and Social Expertise and Rehabilitation. А method for denoising and smoothing of the 3D model surfaces for the manufacture of prosthetic and orthopedic products is proposed. The SNR metric difference SNR – δ-SNR (with averaging by scans and SNR values) and average execution time were calculated. The bilateral filtration method with δ-SNR = 11.3362 dB and a runtime of 8.8900 s showed the highest efficiency.
Conclusion. The proposed methods for the pre-processing stage of 3D optical scans showed high efficiency in the formation of 3D models of prosthesis and orthosis modules. The results obtained can be used for automating the process of manufacturing various prosthetic and orthopedic products, which is particularly relevant in the context of the modern geopolitical situation.
Keywords
About the Authors
A. R. SufelfaRussian Federation
Alisa R. Sufelfa - Head of Laboratory of Albrecht Federal Scientific and Educational Centre of Medical and Social Expertise and Rehabilitation. Postgraduate student of Department of biotechnical systems.
5 F, Professor Popov St., St Petersburg 197022
A. S. Voznesensky
Russian Federation
Alexander S. Voznesensky - Cand. Sci. (2022), senior researcher of SEC DTT.
5 F, Professor Popov St., St Petersburg 197022
D. I. Kaplun
Russian Federation
Dmitry I. Kaplun - Cand. Sci. (2009), Associate Professor of Department of automation and processing.
5 F, Professor Popov St., St Petersburg 197022
References
1. Goldberg C. J., Moore D. P., Fogarty E. E., Dowling F. E. Adolescent Idiopathic Scoliosis: the Effect of Brace Treatment on the Incidence of Surgery. Spine. 2001, vol. 26, no. 1, pp. 42–47. doi: 10.1097/00007632-200101010-00009
2. Condie E., Scott H., Treweek Sh. Lower Limb Prosthetic Outcome Measures: a Review of the Literature 1995 to 2005. J. of Prosthetics and Orthotics. 2006, vol. 18, iss. 6, pp. 13–45. doi: 10.1097/00008526200601001-00004
3. World Health Organization. Guidelines for training personnel in developing countries for prosthetics and orthotics services. Available at: https://www.afro.who.int/sites/default/files/2017-06/who_guidelines_training_ personnel_en.pdf (accessed 11.10.2024)
4. Ikeda A. J., Grabowski A. M., Lindsley A., Sadeghi-Demneh E., Reisinger K. D. A Scoping Literature Review of the Provision of Orthoses and Prostheses in Resource-Limited Environments 2000–2010. Part two: research and outcomes. Prosthetics and Orthotics International. 2014, vol. 38, iss. 5, pp. 343–362. doi: 10.1177/0309364613490443
5. 3DToday 3D Systems iSence. Available at: https://3dtoday.ru/3d-scaners/3d-systems/isense (accessed 11.10.2024)
6. MeshLab 2022.02 Release Notes. Official GitHub Repository. Available at: https://github.com/cnr-isti-vclab/meshlab (accessed 02.08.2023)
7. Rogers B., Bosker G. W., Crawford R. H., Faustini M. C., Neptune R. R., Walden G., Gitter A. J. Advanced Trans-Tibial Socket Fab-Rication Using Selective Laser Sintering. Prosthetics and Orthotics International. 2007, vol. 31, no. 1, pp. 88–100. doi: 10.1080/03093640600983923
8. Samuelsson K. A. M., Töytäri O., Salminen A.-L., Brandt Å. Effects of Lower Limb Prosthesis on Activity, Participation, and Quality of Life: a Systematic Review. Prosthetics and Orthotics International. 2012, vol. 36, iss. 2, pp. 145–158. doi: 10.1177/0309364611432794
9. Sewell P., Noroozi S., Vinney J., Andrews S. Developments in the Transtibial Prosthetic Socket Fitting Process: a Review of Past and Present Research. Prosthetics and Orthotics International. 2000, vol. 24, iss. 2, pp. 97–107. doi: 10.1080/03093640008726532
10. Structure. What are Structure Core's technical specifications? Available at: https://support.structure.io/article/307-what-are-structure-cores-technical-specifications (accessed 03.10.2023)
11. Peyr'e G. The Numerical Tours of Signal Processing-Advanced Computational Signal and Image Processing. IEEE Computing in Science and Engineering. 2011, vol. 13, iss. 4, pp. 94–97. doi: 10.1109/MCSE.2011.71
12. Chen H., Li Zh., Wei M., Wang J. Geometric and Learning-based Mesh Denoising: A Comprehensive Survey. ACM Transactions on Multimedia Computing, Communications and Applications. 2022, vol. 20, iss. 3, art. no. 85, pp. 1–28. doi: 10.1145/3625098
13. Dutta S., Banerjee S., Biswas P. K., Bhowmick P. Mesh Denoising by Improved 3D Geometric Bilateral Filter. Fourth National Conf. on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), Jodhpur, India, 18–21 Dec. 2013. IEEE, 2013, pp. 1–4. doi: 10.1109/NCVPRIPG.2013.6776193
14. Zhang Y., Hamza A. B. Vertex-Based Anisotropic Smoothing of 3D Mesh Data. Canadian Conf. on Electrical and Computer Engineering, Ottawa, Canada, 07–10 May 2006. IEEE, 2006, pp. 202–205. doi: 10.1109/CCECE.2006.277433
15. Yagou H., Ohtake Y., Belyaev A. Mesh Smoothing via Mean and Median Filtering Applied to Face Normal. Geometric Modeling and Processing. Theory and Applications. Proc. of GMP 2002, Wako, Japan, 10–12 July 2002. IEEE. 2002, pp. 124–131. doi: 10.1109/GMAP.2002.1027503
Review
For citations:
Sufelfa A.R., Voznesensky A.S., Kaplun D.I. Mesh Smoothing of 3D Prosthesis and Orthosis Models. Journal of the Russian Universities. Radioelectronics. 2024;27(5):108-117. (In Russ.) https://doi.org/10.32603/1993-8985-2024-27-5-108-117