RECRÉATION DE L'OS MÉTACARPIEN INDEX PAR IMPRESSION 3D À PARTIR DE MODÈLES RADIOLOGIQUESIMPRESSION 3D
DOI :
https://doi.org/10.59277/RRST-EE.2026.2.26Mots-clés :
Fracture, Radiographie, Métacarpien de l’index, Régénération, Image 2D, Impression 3D, Hydroxyapatite de calciumRésumé
Les accidents entraînent fréquemment des fractures, et la régénération osseuse étant difficile, la perte de substance osseuse peut être compensée par des implants. Cet article décrit une technique de remplacement de l'os d'origine à partir d'images radiographiques. L'image radiographique (2D) de l'os fracturé est convertie en une structure 3D. Cette conversion est réalisée grâce à des techniques de traitement d'images. Le processus consiste à remplacer l'os fracturé par un modèle 3D, obtenu à partir d'une image radiographique 2D, à l'aide des logiciels MATLAB et CATIA. Les techniques de traitement d'images suivantes sont utilisées : conversion en niveaux de gris et segmentation par clustering K-Means. Le logiciel CATIA permet ensuite d'obtenir l'image 3D du métacarpien à remplacer. Enfin, une prothèse 3D en hydroxyapatite de calcium, d'une longueur de 56,33 mm, est imprimée.
Références
(1) A. Mehranian, M. Reza, A. Rahmim, H. Zaidi, 3D prior image constrained projection completion for X-ray CT metal artifact reduction, IEEE Transactions on Nuclear Science, 60, 5, pp. 3318–3332 (2013).
(2) G.C. Anzalone, C. Zhang, B. Wijnen, P.G. Sanders, J.M. Pearce, A low-cost open-source metal 3-D printer, IEEE Access, 1, pp. 1–10 (2013).
(3) C. Zhang, Inhibition of furin by bone targeting super paramagnetic iron oxide nanoparticles alleviated breast cancer bone metastasis, 6, 3, pp. 712–720 (2020).
(4) K.P. Divya, S. Arun, A survey on 2D to 3D image and video conversion techniques, International Journal of Research in Advent Technology, 2, pp. 99–102 (2014).
(5) S. Dixit, V.G. Pai, K. Agnani, V.S.R. Priyan, V.C. Radrigues, 3D reconstruction of 2D X-ray images, pp. 1–5 (2019).
(6) H.S.M. Haugen, Discover the body 3D printing and teaching materials for blind and visually impaired children, Future Reflections, pp. 1–10 (2013).
(7) J.L. Herrera, C.R. del-Bianco, N. García, Learning 3D structure from 2D images using LBP features, IEEE International Conference on Image Processing (ICIP), 1, pp. 2022–2025 (2014).
(8) J. Wu, C. Zhang, X. Zhang, Z. Zhang, W.T. Freeman, J.B. Tenenbaum, Learning shape priors for single-view 3D completion and reconstruction, CSAIL, Cambridge, MA, USA, pp. 1–10 (2018).
(9) J. Konrad, M. Wang, P. Ishwar, C. Wu, D. Mukharjee, Learning-based, automatic 2D-to-3D image and video conversion, IEEE Transactions on Image Processing, 22, 9, pp. 3485–3496 (2013).
(10) R. Parasuraman, R. Varadhan, Early identification of blood cancer through automated anomaly detection with a convolutional neural network, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 70, pp. 415–420 (2025).
(11) A. Appathurai, A.S.I. Tinu, M. Narayanaperumal, MEG and PET images-based brain tumor detection using Kapur's Otsu segmentation and sooty optimized Mobilenet classification, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 69, 3, pp. 359–364 (2024).
(12) National Library of Medicine, MedlinePlus, Bethesda, MD, USA, pp. 1–10 (2020).
(13) M. Okoli, Metacarpal bony dimensions related to headless compression screw sizes, Journal of Hand and Microsurgery, 1, pp. 39–44 (2020).
(14) R. Phan, R. Rzeszutek, D. Androutsos, Semi-automatic 2D to 3D image conversion using scale-space random walks and a graph cuts based depth prior, 18th IEEE International Conference on Image Processing, 1, pp. 865–868 (2011).
(15) P. Markelj, D. Tomaževič, B. Likar, F. Pernuš, A review of 3D/2D registration methods for image-guided interventions, Medical Image Analysis, 16, 3, pp. 642–661 (2012).
(16) E.S. Akpan, M. Dauda et al., Box-Behnken experimental design for the process optimization of catfish bones derived hydroxyapatite: A pedagogical approach, Materials Chemistry and Physics, 272, pp. 1–10 (2022).
(17) U. Mitrovič, Ž. Špiclin, B. Likar, F. Pernuš, 3D-2D registration of cerebral angiograms, a method and evaluation on clinical images, IEEE Transactions on Medical Imaging, 32, pp. 1550–1563 (2013).
(18) W. Huang et al., Toward naturalistic 2D-to-3D conversion, IEEE Transactions on Image Processing, 24, pp. 724–733 (2015).
(19) A. Ramaiah, P.D. Balasubramanian, A. Appathurai, N.A. Muthukumaran, Detection of Parkinson's disease via Clifford gradient-based recurrent neural network using multi-dimensional data, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 69, 1, pp. 103–108 (2024).
(20) E. Menendez-Proupin, S. Cervantes-Rodríguez, R. Osorio-Pulgar, M. Franco-Cisterna, H. Camacho-Montes, M.E. Fuentes, Computer simulation of elastic constants of hydroxyapatite and fluorapatite, Journal of the Mechanical Behavior of Biomedical Materials, 4, 7, pp. 1011–1020 (2011).
(21) D.O. Obada et al., Fabrication of novel kaolin-reinforced hydroxyapatite scaffolds with robust compressive strengths for bone regeneration, Applied Clay Science, 1, pp. 215–224 (2021).
(22) E.S. Akpan et al., A facile synthesis method and fracture toughness evaluation of catfish bones-derived hydroxyapatite, MRS Advances, 5, 26, pp. 1357–1366 (2020).
(23) H. Shi, Z. Zhou et al., Hydroxyapatite-based materials for bone tissue engineering: A brief and comprehensive introduction, Crystals, 11, pp. 149–162 (2021).
(24) M. Kumar, S. Ramkumar, R. Mageswaran, R. Balakrishnan, Effective feature extraction method for unconstrained environment: local binary pattern or local ternary pattern, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 69, 4, pp. 443–448 (2024).
(25) S. Ramkumar, R.K. M. et al., Programmed for automatic bone disorder clustering based on cumulative calcium prediction for feature extraction, Clinical Laboratory, 68, 8, pp. 1579–1588 (2022).
(26) O.A. Osuchukwu, A. Salihi, I. Abdullahi, D.O. Obada et al., Datasets on the elastic and mechanical properties of hydroxyapatite: A first principle investigation, experiments, and pedagogical perspective, Data in Brief, 48, pp. 1–11 (2023).
(27) R.I. Martin, P.W. Brown, Mechanical properties of hydroxyapatite formed at physiological temperature, Journal of Materials Science: Materials in Medicine, 6, pp. 138–143 (1995).
(28) E. Menendez-Proupin, S. Cervantes-Rodríguez, R. Osorio-Pulgar, M. Franco-Cisterna, H. Camacho-Montes, M.E. Fuentes, Computer simulation of elastic constants of hydroxyapatite and fluorapatite, Journal of the Mechanical Behavior of Biomedical Materials, 4, 7, pp. 1011–1020 (2011).
(29) I. Ielo, G. Calabrese, G. De Luca, S. Conoci, Recent advances in hydroxyapatite-based biocomposites for bone tissue regeneration in orthopedics, International Journal of Molecular Sciences, 23, 17, pp. 9721–9733 (2022).
(30) Z. Li, Q. Wang, G. Liu, A review of 3D printed bone implants, Micromachines, 13, pp. 1–25 (2022).
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