Informatica logo


Login Register

  1. Home
  2. Issues
  3. Volume 35, Issue 4 (2024)
  4. A Novel Radial Basis Function Descriptio ...

Informatica

Information Submit your article For Referees Help ATTENTION!
  • Article info
  • Full article
  • More
    Article info Full article

A Novel Radial Basis Function Description of a Smooth Implicit Surface for Musculoskeletal Modelling
Volume 35, Issue 4 (2024), pp. 721–750
Martin Cervenka ORCID icon link to view author Martin Cervenka details   Josef Kohout ORCID icon link to view author Josef Kohout details   Bogdan Lipus ORCID icon link to view author Bogdan Lipus details  

Authors

 
Placeholder
https://doi.org/10.15388/24-INFOR571
Pub. online: 16 October 2024      Type: Research Article      Open accessOpen Access

Received
1 June 2024
Accepted
1 September 2024
Published
16 October 2024

Abstract

As musculoskeletal illnesses continue to increase, practical computerised muscle modelling is crucial. This paper addresses this concern by proposing a mathematical model for a dynamic 3D geometrical surface representation of muscles using a Radial Basis Function (RBF) approximation technique. The objective is to obtain a smoother surface while minimising data use, contrasting it from classical polygonal (e.g. triangular) surface mesh models or volumetric (e.g. tetrahedral) mesh models. The paper uses RBF implicit surface description to describe static surface generation and dynamic surface deformations based on its spatial curvature preservation during the deformation. The novel method is tested on multiple data sets, and the experiments show promising results according to the introduced metrics.

References

 
Abderrazak, K., Benabid, Y. (2022). In: Realistic modeling of shoulder muscle for use in musculoskeletal model. The 13th Conference on Mechanical Engineering CME2022.
 
Afiatdoust, F., Esmaeilbeigi, M. (2015). Optimal variable shape parameters using genetic algorithm for radial basis function approximation. Ain Shams Engineering Journal, 6(2), 639–647. https://doi.org/10.1016/j.asej.2014.10.019.
 
Angles, B., Rebain, D., Macklin, M., Wyvill, B., Barthe, L., Lewis, J., Von Der Pahlen, J., Izadi, S., Valentin, J., Bouaziz, S., Tagliasacchi, A. (2019). VIPER: volume invariant position-based elastic rods. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 2(2) 1–26. https://doi.org/10.1145/3340260.
 
Cani-Gascuel, M., Desbrun, M. (1997). Animation of deformable models using implicit surfaces. IEEE Transactions on Visualization and Computer Graphics, 3(1), 39–50. https://doi.org/10.1109/2945.582343.
 
Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R. (2001). Reconstruction and representation of 3D objects with radial basis functions. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH ‘01. Association for Computing Machinery, New York, NY, USA, pp. 67–76. https://doi.org/10.1145/383259.383266.
 
Cervenka, M., Smolik, M., Skala, V. (2019). A new strategy for scattered data approximation using radial basis functions respecting points of inflection. In: Misra, S., Gervasi, O., Murgante, B., Stankova, E., Korkhov, V., Torre, C., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O., Tarantino, E. (Eds.), Computational Science and Its Applications – ICCSA 2019. Springer International Publishing, Cham, pp. 322–336.
 
Cervenka, M., Havlicek, O., Kohout, J., Váša, L. (2023). Computerised muscle modelling and simulation for interactive applications. In: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) – GRAPP. SciTePress, pp. 214–221. https://doi.org/10.5220/0011688000003417.
 
Cieza, A., Causey, K., Kamenov, K., Hanson, S., Chatterji, S., Vos, T. (2020). Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet, 396(10267), 2006–2017. https://doi.org/10.1016/S0140-6736(20)32340-0.
 
Clapés, M., González Hidalgo, M., Torres, A., Palmer-Rodríguez, P. (2008). Interactive constrained deformations of NURBS surfaces: N-SCODEF. In: Articulated Motion and Deformable Objects. AMDO 2008, pp. 359–369. https://doi.org/10.1007/978-3-540-70517-8_35.
 
Delp, S., Blemker, S. (2005). Three-dimensional representation of complex muscle architectures and geometries. Annals of Biomedical Engineering, 33(5), 661–673. https://doi.org/10.1007/s10439-005-1433-7.
 
Dvořák, J., Káčereková, Z., Vanecek, P., Hruda, L., Váša, L. (2021). As-rigid-as-possible volume tracking for time-varying surfaces. Computers & Graphics, 102, 329–338. https://doi.org/10.1016/j.cag.2021.10.015.
 
Fasser, M., Jokeit, M., Kalthoff, M., Gomez Romero, D.A., Trache, T., Snedeker, J.G., Farshad, M., Widmer, J. (2021). Subject-specific alignment and mass distribution in musculoskeletal models of the lumbar spine. Frontiers in Bioengineering and Biotechnology, 9, 721042.
 
Goyanes, E., de Moura, J., Fernández-Vigo, J.I., Fernández-Vigo, J.A., Novo, J., Ortega, M. (2024). Automatic simultaneous ciliary muscle segmentation and biomarker extraction in AS-OCT images using deep learning-based approaches. Biomedical Signal Processing and Control, 90, 105851. https://doi.org/10.1016/j.bspc.2023.105851. https://www.sciencedirect.com/science/article/pii/S1746809423012843.
 
Hájková, J., Kohout, J. (2014). Human body model movement support: automatic muscle control curves computation. In: Combinatorial Image Analysis. IWCIA 2014, pp. 196–211. https://doi.org/10.1007/978-3-319-07148-0_18.
 
Hardy, R.L. (1990). Theory and applications of the multiquadric-biharmonic method 20 years of discovery 1968–1988. Computers & Mathematics with Applications, 19(8), 163–208. https://doi.org/10.1016/0898-1221(90)90272-L.
 
Hardy, R.L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of Geophysical Research, 76(8), 1905–1915. https://doi.org/10.1029/JB076i008p01905.
 
Heinrich, D., Bogert, A., Mössner, M., Nachbauer, W. (2023). Model-based estimation of muscle and ACL forces during turning maneuvers in alpine skiing. Scientific Reports, 13, 9026. https://doi.org/10.1038/s41598-023-35775-4.
 
Hill, A. (1938). The heat of shortening and the dynamic constants of muscle. Proceedings of the Royal Society of London. Series B, Biological Sciences, 126(843), 136–195.
 
Hilton, A., Stoddart, A.J., Illingworth, J., Windeatt, T. (1996). Marching triangles: range image fusion for complex object modelling. In: Proceedings of 3rd IEEE International Conference on Image Processing, Vol. 2, pp. 381–3842. https://doi.org/10.1109/ICIP.1996.560840.
 
Janák, T., Kohout, J. (2014). Deformable muscle models for motion simulation. In: Proceedings of the 9th International Conference on Computer Graphics Theory and Applications – GRAPP, (VISIGRAPP 2014). SciTePress, pp. 301–311. https://doi.org/10.5220/0004678903010311.
 
Joseph, V.R., Hung, Y., Sudjianto, A. (2008). Blind kriging: a new method for developing metamodels. Journal of Mechanical Design, 130(3), 031102. https://doi.org/10.1115/1.2829873.
 
Kaymaz, I. (2005). Application of kriging method to structural reliability problems. Structural Safety, 27(2), 133–151. https://doi.org/10.1016/j.strusafe.2004.09.001.
 
Kedadria, A., Benabid, Y., Remil, O., Benaouali, A., May, A., Ramtani, S. (2023). A shoulder musculoskeletal model with three-dimensional complex muscle geometries. Annals of Biomedical Engineering, 51(5), 1079–1093. https://doi.org/10.1007/s10439-023-03189-y.
 
Kellnhofer, P., Kohout, J. (2012). Time-convenient deformation of musculoskeletal system. In: Proceedings of ALGORITMY 2012, pp. 1–10.
 
Kohout, J., Cervenka, M. (2022). Non-planar surface shape reconstruction from a point cloud in the context of muscles attachments estimation. In: Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) – GRAPP. SciTePress, pp. 236–243. https://doi.org/10.5220/0010869600003124.
 
Lavoie, P., Ionescu, D., Petriu, E. (2006). Constructing 3D virtual reality objects from 2D images of real objects using NURBS. In: 2007 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, Ostuni, Italy, June 25–27, 2007, pp. 117–124. https://doi.org/10.1109/VECIMS.2007.4373940.
 
Lee, D., Glueck, M., Khan, A., Fiume, E., Jackson, K. (2012). Modeling and simulation of skeletal muscle for computer graphics: a survey. Foundations and Trends® in Computer Graphics and Vision, 7(4), 229–276. https://doi.org/10.1561/0600000036.
 
Lorensen, W.E., Cline, H.E. (1987). Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Computer Graphics, 21(4), pp. 163–169. https://doi.org/10.1145/37401.37422.
 
Macklin, M., Müller, M., Chentanez, N. (2016). XPBD: position-based simulation of compliant constrained dynamics. In: Proceedings of the 9th International Conference on Motion in Games. MIG ’16. Association for Computing Machinery, New York, NY, USA, pp. 49–54. https://doi.org/10.1145/2994258.2994272.
 
Majdisova, Z., Skala, V. (2017). Radial basis function approximations: comparison and applications. Applied Mathematical Modelling, 51, 728–743. https://doi.org/10.1016/j.apm.2017.07.033.
 
Modenese, L., Renault, J.-B. (2021). Automatic generation of personalized skeletal models of the lower limb from three-dimensional bone geometries. Journal of Biomechanics, 116, 110186. https://doi.org/10.1016/j.jbiomech.2020.110186.
 
Müller, M., Heidelberger, B., Hennix, M., Ratcliff, J. (2007). Position based dynamics. Journal of Visual Communication and Image Representation, 18(2), 109–118. https://doi.org/10.1016/j.jvcir.2007.01.005.
 
Ni, N., He, K., Wang, L., Jiang, J., Chen, Z. (2023). Modeling of human muscle and its deformation. Computer Methods in Biomechanics and Biomedical Engineering, 27(3), 365–377. https://doi.org/10.1080/10255842.2023.2186160.
 
Nie, M., Wan, Y., Zhou, A. (2022). Real-time NURBS interpolation under multiple constraints. Computational Intelligence and Neuroscience, 2022, 1–15. https://doi.org/10.1155/2022/7492762.
 
Oliver, M.A., Webster, R. (1990). Kriging: a method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4(3), 313–332. https://doi.org/10.1080/02693799008941549.
 
Orr, M.J.L. (1995). Regularization in the selection of radial basis function centers. Neural Computation, 7(3), 606–623. https://doi.org/10.1162/neco.1995.7.3.606.
 
Romeo, M., Monteagudo, C., Sánchez-Quirós, D. (2018). Muscle simulation with extended position based dynamics. In: García-Fernández, I., Ureña, C. (Eds.), Spanish Computer Graphics Conference (CEIG). The Eurographics Association, pp. 134–146. https://doi.org/10.2312/ceig.20181146.
 
Sakata, S., Ashida, F., Zako, M. (2004). An efficient algorithm for Kriging approximation and optimization with large-scale sampling data. Computer Methods in Applied Mechanics and Engineering, 193(3), 385–404. https://doi.org/10.1016/j.cma.2003.10.006.
 
Sarra, S.A., Sturgill, D. (2009). A random variable shape parameter strategy for radial basis function approximation methods. Engineering Analysis with Boundary Elements, 33(11), 1239–1245. https://doi.org/10.1016/j.enganabound.2009.07.003.
 
Skala, V. (2017). Least square method robustness of computations: what is not usually considered and taught. In: 2017 Federated Conference on Computer Science and Information Systems, pp. 537–541. https://doi.org/10.15439/2017F7.
 
Skala, V., Cervenka, M. (2019). Novel RBF approximation method based on geometrical properties for signal processing with a new RBF function: experimental comparison. In: 2019 IEEE 15th International Scientific Conference on Informatics. https://doi.org/10.1109/Informatics47936.2019.9119276.
 
Skala, V., Karim, S.A.A., Zabran, M. (2020). Radial basis function approximation optimal shape parameters estimation. In: Computational Science – ICCS 2020. Springer International Publishing, Cham, pp. 309–317.
 
Sorkine, O., Alexa, M. (2007). As-rigid-as-possible surface modeling. In: Belyaev, A., Garland, M. (Eds.), Geometry Processing. The Eurographics Association, pp. 109–116. https://doi.org/10.2312/SGP/SGP07/109-116.
 
Sánchez-Reyes, J., Chacón, J. (2020). How to make impossible objects possible: anamorphic deformation of textured NURBS. Computer Aided Geometric Design, 78, 101826. https://doi.org/10.1016/j.cagd.2020.101826.
 
Terzopoulos, D., Qin, H. (1994). Dynamic NURBS with geometric constraints for interactive sculpting. ACM Transactions on Graphics, 13(2), 103–136. https://doi.org/10.1145/176579.176580.
 
Valente, G., Martelli, S., Taddei, F., Farinella, G., Viceconti, M. (2012). Muscle discretization affects the loading transferred to bones in lower-limb musculoskeletal models. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 226(2), 161–169.
 
Wang, B., Matcuk, G., Barbič, J. (2021). Modeling of personalized anatomy using plastic strains. ACM Transactions on Graphics, 40(2). https://doi.org/10.1145/3443703.
 
Wright, G.B. (2003). Radial Basis Function Interpolation: Numerical and Analytical Developments. PhD thesis, University of Colorado at Boulder, Boulder, CO, USA. AAI3087597.
 
Ye, D., Jiang, X., Huo, G., Su, C., Lu, Z., Wang, B., Zheng, Z. (2020). A physical process driven digital terrain model generating method based on D-NURBS. IEEE Access, 8, 3115–3122. https://doi.org/10.1109/ACCESS.2019.2962385.
 
Zhang, M., Qin, H. (2001). Hierarchical D-NURBS surfaces and their physics-based sculpting. In: Proceedings International Conference on Shape Modeling and Applications, pp. 257–266. https://doi.org/10.1109/SMA.2001.923397.
 
Zhang, Z.-Y., Sun, Z.-J., Yang, Y.-H., Lin, H. (2022). Towards a better prediction of subcellular location of long non-coding RNA. Frontiers of Computer Science, 16(5), 165903. https://doi.org/10.1007/s11704-021-1015-3.
 
Zhao, W., San, Y. (2011). RBF neural network based on q-Gaussian function in function approximation. Frontiers of Computer Science in China, 5(4), 381–386. https://doi.org/10.1007/s11704-011-1041-7.

Biographies

Cervenka Martin
https://orcid.org/0000-0001-9625-1872
cervemar@kiv.zcu.cz

M. Červenka is a computer science doctoral student at West Bohemia University, Pilsen, Czech Republic. He earned his master’s in computer science from West Bohemia University, Pilsen, Czech Republic, in 2019. During this period, his research focused on muscle modelling approaches, radial basis function interpolation, and approximation techniques. He is affiliated with West Bohemia University and continues to be deeply engaged in applying radial basis functions in muscle modelling.

Kohout Josef
https://orcid.org/0000-0002-3231-2573

J. Kohout is an associate professor in computer science at West Bohemia University, Pilsen, Czech Republic. His expertise lies in the field of computer graphics and medical informatics. He possesses a skill set encompassing mesh processing, data visualisation, scientific visualisation, medical image processing, simulation, and more. Currently, his research is focused on diverse muscle modelling methodologies.

Lipus Bogdan
https://orcid.org/0000-0001-6529-4263

B. Lipuš is an assistant professor at the Faculty of Electrical Engineering and Computer Sciences, University of Maribor. Currently, he is a member of the Laboratory for Geospatial Modelling, Multimedia and Artificial Intelligence. His research interests include lidar data processing, remote sensing, data compression, computer graphics, computer-aided geometric design, and image reconstruction.


Full article PDF XML
Full article PDF XML

Copyright
© 2024 Vilnius University
by logo by logo
Open access article under the CC BY license.

Keywords
radial basis function muscle model gradient descent curvature mean curvature Gaussian RBF

Funding
This research was supported by the Czech Science Foundation, project number 23-04622L, by the Slovene Research Agency under Research Project J2-4458 and Research Programme P2-0041 and by the Ministry of Education, Youth and Sports of the Czech Republic, project SGS-2022-015.

Metrics
since January 2020
304

Article info
views

246

Full article
views

130

PDF
downloads

44

XML
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
  • Copyright © 2023 Vilnius University

About

  • About journal

For contributors

  • OA Policy
  • Submit your article
  • Instructions for Referees
    •  

    •  

Contact us

  • Institute of Data Science and Digital Technologies
  • Vilnius University

    Akademijos St. 4

    08412 Vilnius, Lithuania

    Phone: (+370 5) 2109 338

    E-mail: informatica@mii.vu.lt

    https://informatica.vu.lt/journal/INFORMATICA
Powered by PubliMill  •  Privacy policy