Diseño de una metodología para el mejoramiento de procesos odontológicos basados en técnicas de segmentación en odontología digital

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dc.contributor.advisorGómez-Mendoza, Juan Bernardo
dc.contributor.advisorGuevara Perez, Sonia V.
dc.contributor.authorTamayo-Quintero, Juan David
dc.contributor.cvlacTamayo-Quintero, Juan David [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001523579]spa
dc.contributor.googlescholarTamayo-Quintero, Juan David [https://scholar.google.com.co/citations?user=Bkfzfk4AAAAJ&hl=en]spa
dc.contributor.orcidTamayo-Quintero, Juan David [https://orcid.org/0000-0002-0159-1573]spa
dc.contributor.researchgroupSHAC - Computación aplicada suave y duraspa
dc.date.accessioned2024-11-05T15:20:08Z
dc.date.available2024-11-05T15:20:08Z
dc.date.issued2024
dc.descriptionfotografías, ilustraciones, tablasspa
dc.description.abstractThis thesis proposes a methodology for supporting orthodontic treatments through image processing techniques in digital dentistry. Led by Juan David Tamayo Quintero at the Universidad Nacional de Colombia, the research aims to improve orthodontic processes using artificial vision and image processing. The study focuses on developing tools for dental arch shape classification and analysis, employing image processing methods. The methodology integrates data structures for dental models, including various file formats like PLY, STL, OBJ, and PCD. Point cloud data representation is explored, emphasizing neighborhood and curvature estimation techniques. The document introduces the creation of a database comprising digitized dental models and outlines arch form classification methodologies, including ovoid, tapered, and square forms, along with morphometric templates. Ground truth determination methods and statistical analyses for evaluating model accuracy are also presented. The data analysis and implementation section details techniques for representing data in a 2.5D space, image projections, and segmentation methods, including watershed segmentation. Two software tools, DentalArch and DentiShape3D, were developed to aid in dental arch analysis, incorporating machine learning and image processing techniques. The conclusion reflects on achievements in image processing and database construction, highlighting future work areas such as enhancing sample diversity and reducing evaluator biases. The thesis provides a contribution to the field of digital dentistry, offering methodologies and tools to improve orthodontic processes through image processing techniques​ (Texto tomado de la fuente).eng
dc.description.abstractEsta tesis propone una metodología para apoyar los tratamientos de ortodoncia a través de técnicas de procesamiento de imágenes en la odontología digital. Dirigida por Juan David Tamayo Quintero en la Universidad Nacional de Colombia, la investigación tiene como objetivo mejorar los procesos de ortodoncia utilizando visión artificial y procesamiento de imágenes. El estudio se centra en desarrollar herramientas para la clasificación y análisis de la forma del arco dental, empleando métodos de procesamiento de imágenes y técnicas de aprendizaje automático. La metodología integra estructuras de datos para modelos dentales, incluyendo varios formatos de archivo como PLY, STL, OBJ y PCD. Se explora la representación de datos de nube de puntos, haciendo hincapié en técnicas de estimación de vecindario y curvatura. El documento presenta la creación de una base de datos que comprende modelos dentales digitalizados y describe metodologías de clasificación de forma de arco, incluyendo formas ovoide, cónica y cuadrada, junto con plantillas morfométricas. También se presentan métodos de determinación de la verdad fundamental y análisis estadísticos para evaluar la precisión del modelo. La sección de análisis de datos e implementación detalla técnicas para representar datos en un espacio 2.5D, proyecciones de imágenes y métodos de segmentación, incluida la segmentación de cuenca hidrográfica. Se desarrollaron dos herramientas de software, DentalArch y DentiShape3D, para ayudar en el análisis del arco dental, incorporando técnicas de aprendizaje automático y procesamiento de imágenes. La conclusión reflexiona sobre los logros en el procesamiento de imágenes y la construcción de bases de datos, destacando áreas de trabajo futuro como mejorar la diversidad de muestras y reducir los sesgos del evaluador. La tesis proporciona una contribución al campo de la odontología digital, ofreciendo metodologías y herramientas para mejorar los procesos de ortodoncia a través de técnicas de procesamiento de imágenesspa
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesspa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ingenieríaspa
dc.description.researchareaTratamiento, modelado y visualizacion de datosspa
dc.format.extentxii, 148 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/87149
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automáticaspa
dc.relation.referencesH. Yang, F. Li, and L. Li, “Comparison of maxillary anterior mathematical proportions among 3 dental arch forms,” The Journal of Prosthetic Dentistry, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0022391321006594spa
dc.relation.referencesB. Kim, M. Bayome, Y. Kim, S. Baek, and S. Han, “Comparison of overjet among 3 arch types in normal occlusion,” American Journal of Orthodontics and Dentofacial Orthopedics, 2011. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S0889540610009327spa
dc.relation.referencesJ. Tamayo-Quintero, J. Gómez-Mendoza, and S. Guevara-Pérez, “Dentalarch: Ai-based arch shape detection in orthodontics,” Applied Sciences, 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/6/2567spa
dc.relation.referencesI. Tajik, N. Mushtaq, and M. Khan, “Arch forms among different angle classifications a–study.” Pakistan Oral & Dental Journal, 2011. [Online]. Availa- ble: https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site& authtype=crawler&jrnl=10128700&AN=66962276spa
dc.relation.referencesM. Bae, J. Park, and N. Kim, “Fully automated estimation of arch forms in cone-beam ct with cubic b-spline approximation: Evaluation of digital dental models with missing teeth,” Computers in Biology and Medicine, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0010482521000500spa
dc.relation.referencesJ. Pillai, R. Patel, and A. Banker, “Correlation between maxillary central incisor crown form and maxillary dental arch form: A model-based morphometric, cross-sectional study,” Journal of Forensic Sciences, 2018. [Online]. Available: https://journals.lww. com/jfsm/fulltext/2018/04020/correlation between maxillary central incisor.2.aspxspa
dc.relation.referencesM. Toodehzaeim and S. Mostafavi, “Dental arch morphology in iranian population,” Iranian Journal of Orthodontics, vol. 11, no. 1, p. e245697, 2016, access date: 2024-03-25. [Online]. Available: https://www.ijorth.com/article 245697.htmlspa
dc.relation.referencesM. Slaj, S. Spalj, D. Jelusic, and M. Slaj, “Discriminant factor analysis of dental arch dimensions with 3-dimensional virtual models,” American Journal of Orthodontics and Dentofacial Orthopedics, vol. 140, no. 4, pp. e181–e192, 2011. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0889540611006792spa
dc.relation.referencesK. Arai and L. Will, “Subjective classification and objective analysis of the mandibular dental-arch form of orthodontic patients,” American Journal of Orthodontics and Dentofacial Orthopedics, vol. 140, no. 4, pp. e197–e204, 2011. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0889540610010243spa
dc.relation.referencesJ. Budiman, “Neural network in developing software for identifying arch form,” International Journal of Artificial Intelligence Applications, vol. 4, no. 2, pp. 1–11, 2013. [Online]. Available: https://www.researchgate.net/publication/307671113 Neural Network in Developing Software for Identifying Arch Formspa
dc.relation.referencesA. Ender, T. Attin, and A. Mehl, “In vivo precision of conventional and digital methods of obtaining complete-arch dental impressions,” The Journal of prosthetic dentistry, 2016.spa
dc.relation.referencesM. Cicci`u, L. Fiorillo, C. D’Amico, and D. Gambino, “3d digital impression systems compared with traditional techniques in dentistry: a recent data systematic review,” Materials, 2020.spa
dc.relation.referencesA. Al-Khatib, Z. Rajion, and M. Sam’an, “Validity and reliability of tooth size and dental arch measurements: a stereo photogrammetric study,” Australasian, 2012spa
dc.relation.referencesC. Lane and W. H. Jr, “Completing the 3-dimensional picture,” American journal of orthodontics and dentofacial, 2008spa
dc.relation.referencesC. Keul and J. G¨uth, “Accuracy of full-arch digital impressions: an in vitro and in vivo comparison,” Clinical oral investigations, 2020.spa
dc.relation.referencesA. Boltunov, Y. Brailov, F. Chelnokov, and R. Roschin, “Digital dental modeling,” US Patent, 2014. [Online]. Available: https://patents.google.com/patent/US8896592B2/enspa
dc.relation.referencesE. Rekow, “Digital dentistry: The new state of the art—is it disruptive or destructive?” Dental Materials, 2020. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/S0109564119308061spa
dc.relation.referencesL. Lin, Y. Fang, Y. Liao, G. Chen, and C. Gao, “3d printing and digital processing techniques in dentistry: a review of literature,” Advanced Engineering, 2019. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1002/adem.201801013spa
dc.relation.referencesK. Hung, A. Yeung, and R. Tanaka, “Current applications, opportunities, and limitations of ai for 3d imaging in dental research and practice,” International Journal of, 2020. [Online]. Available: https://www.mdpi.com/1660-4601/17/12/4424spa
dc.relation.referencesS. Logozzo, G. Franceschini, and A. Kilpela, “A comparative analysis of intraoral 3d digital scanners for restorative dentistry,” Journal of Medical Technology, 2011. [Online]. Available: https://flore.unifi.it/bitstream/2158/438866/1/ARTICOLO%20ULTIMA% 20SINTESI rev.docspa
dc.relation.referencesG. Reesu, B. Woodsend, S. Mˆanica, G. Revie et al., “Automated identification from dental data (autoidd): A new development in digital forensics,” Forensic Science International, 2020. [Online]. Available: https://www.sciencedirect.com/ science/article/pii/S0379073820300803spa
dc.relation.references] R. Chauhan, T. Shah, D. Shah, T. Gohil et al., “An overview of image processing for dental diagnosis,” Innovation and Research in BioMedical Engineering, 2023.spa
dc.relation.referencesM. Marshall and B. Hultgren, “Method and apparatus for combining 3d dental scans with other 3d data sets,” Patent, 2012. [Online]. Available: https://patents.google.com/patent/US8199988B2/enspa
dc.relation.referencesW. Bholsithi, N. Phichitchaiphan, and C. Sinthanayothin, “Online dental information database for dental identification system,” Proceeding - 5th International Conference on Computer Sciences and Convergence Information Technology, ICCIT 2010, pp. 278–283, 2010.spa
dc.relation.referencesR. DeLong, M. Heinzen, J. Hodges et al., “Accuracy of a system for creating 3d computer models of dental arches,” Journal of Dental Research, 2003. [Online]. Available: https://journals.sagepub.com/doi/abs/10.1177/15440591030820060spa
dc.relation.referencesU. Orth and V. Wedler, “Method for obtaining a position match of 3d data sets in a dental cad/cam system,” Patent, 2010. [Online]. Available: https://patents.google.com/patent/US7796811B2/enspa
dc.relation.referencesX. Zhong, D. Yu, Y. S. Wong, T. Sim, W. Lu et al., “3d dental biometrics: Alignment and matching of dental casts for human identification,” Computers in Biology and Medicine, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S016636151300119Xspa
dc.relation.referencesT. Alghazzawi, “Advancements in cad/cam technology: Options for practical implementation,” Journal of Prosthodontic Research, 2016. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1883195816000098spa
dc.relation.referencesS. Pillai, A. Upadhyay, P. Khayambashi, I. Farooq et al., “Dental 3d-printing: transferring art from the laboratories to the clinics,” Polymers, 2021. [Online]. Available: https://www.mdpi.com/2073-4360/13/1/157spa
dc.relation.referencesV. Ronsivalle, F. Ruiz, A. L. Giudice, E. Carli, P. Venezia et al., “From reverse engineering software to cad-cam systems: how digital environment has influenced the clinical applications in modern dentistry,” Applied Sciences, 2023. [Online]. Available: https://www.mdpi.com/2076-3417/13/8/4986spa
dc.relation.referencesJ. Pirskanen, Dental health care technologies: factors affecting technology adoption and latest information technology solutions. Aalto University, 2018. [Online]. Available: https://aaltodoc.aalto.fi/handle/123456789/32993spa
dc.relation.referencesM. Alauddin, A. Baharuddin, and M. M. Ghazali, “The modern and digital transformation of oral health care: A mini review,” Healthcare, 2021. [Online]. Available: https://www.mdpi.com/2227-9032/9/2/118spa
dc.relation.referencesW. Schroeder, K. Martin, and B. Lorensen, The Visualization Toolkit: An Object- Oriented Approach to 3D Graphics, 4th ed. Clifton Park, NY, USA: Kitware, Inc., 2006.spa
dc.relation.referencesG. Turk, “The PLY Polygon File Format,” https://web.archive.org/web/ 20161204152348/http://www.dcs.ed.ac.uk/teaching/cs4/www/graphics/Web/ply. html, 1994, permission to copy the documentation was explicitly granted.spa
dc.relation.referencesP. Bourke, “STL format,” October 1999, written by Paul Bourke.spa
dc.relation.referencesWavefront, Inc., “MTL material format (Lightwave, OBJ),” http://paulbourke.net/ dataformats/mtl/, October 1995, excerpt from FILE FORMATS, Version 4.2, Copyright 1995, Alias—Wavefront, Inc. Original documentation created by: Diane Ramey, Linda Rose, and Lisa Tyerman.spa
dc.relation.referencesW. Technologies, “Copy of Appendix B1 of Advanced Visualizer manual,” http://www. cs.utah.edu/∼boulos/cs3505/obj spec.pdf, 1992, text as distributed with version 3 of Wavefront’s Advanced Visualizer software.spa
dc.relation.referencesPoint Cloud Library, “The PCD (Point Cloud Data) file format,” https://pcl. readthedocs.io/projects/tutorials/en/latest/pcd file format.html.spa
dc.relation.referencesR. Aburashed, 3D printing in Clinical Diagnostics: A Versatile Tool to Develop Testing Devices for the Diagnosis of Infectious Diseases. prism.ucalgary.ca, 2023. [Online]. Available: https://prism.ucalgary.ca/bitstreams/ 01abce21-e198-42c7-b094-f29caf463a27/downloadspa
dc.relation.referencesP. Gil, C. Mateo Agull´o, J. Pomares, G. J. Garcia, and F. Torres, “Reconocimiento de objetos 3d con descriptores de superficie,” 2016-06.spa
dc.relation.referencesS. Arya and D. M. Mount, “Algorithms for fast vector quantization,” in Data Compres- sion Conference, 1993. DCC’93. IEEE, 1993, pp. 381–390.spa
dc.relation.referencesS. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, “An optimal algorithm for approximate nearest neighbor searching fixed dimensions,” Journal of the ACM (JACM), vol. 45, no. 6, pp. 891–923, 1998.spa
dc.relation.referencesM. Muja and D. G. Lowe, “Fast approximate nearest neighbors with automatic algo- rithm configuration.” VISAPP (1), vol. 2, no. 331-340, p. 2, 2009.spa
dc.relation.referencesN. Bhatia et al., “Survey of nearest neighbor techniques,” arXiv preprint ar- Xiv:1007.0085, 2010.spa
dc.relation.referencesA. Nuchter, K. Lingemann, and J. Hertzberg, “Cached kd tree search for icp algorithms,” in 3-D Digital Imaging and Modeling, 2007. 3DIM’07. Sixth International Conference on. IEEE, 2007, pp. 419–426.spa
dc.relation.referencesC. Silpa-Anan and R. Hartley, “Optimised kd-trees for fast image descriptor matching,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008, pp. 1–8.spa
dc.relation.referencesR. Rusu and S. Cousins, “3d is here: Point cloud library (),” in Robotics and Automation (ICRA), 2011 IEEE International Conference on, May 2011, pp. 1–4.spa
dc.relation.referencesG. Turk and M. Levoy, “Zippered polygon meshes from range images,” in Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, ser. SIGGRAPH ’94. New York, NY, USA: ACM, 1994, pp. 311–318. [Online]. Available: http://doi.acm.org/10.1145/192161.192241spa
dc.relation.referencesJ.-F. Lalonde, R. Unnikrishnan, N. Vandapel, and M. Hebert, “Scale selection for clas- sification of point-sampled 3d surfaces,” in 3-D Digital Imaging and Modeling, 2005. 3DIM 2005. Fifth International Conference on. IEEE, 2005, pp. 285–292.spa
dc.relation.referencesN. J. Mitra and A. Nguyen, “Estimating surface normals in noisy point cloud data,” in Proceedings of the nineteenth annual symposium on Computational geometry. ACM, 2003, pp. 322–328.spa
dc.relation.referencesR. B. Rusu, “Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments,” KI-K¨unstliche Intelligenz, pp. 1–4, 2010.spa
dc.relation.referencesJ. Tamayo-Quintero, “A Contribution to Semi-automatic Segmentation of Point Clouds,” Tesis, Universidad Nacional de Colombia, 2015.spa
dc.relation.referencesC. M. Shakarji, “Least-squares fitting algorithms of the nist algorithm testing system,” Journal of research of the National Institute of Standards and Technology, vol. 103, no. 6, p. 633, 1998.spa
dc.relation.referencesG. Hetzel, B. Leibe, P. Levi, and B. Schiele, “3d object recognition from range images using local feature histograms,” in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 2. IEEE, 2001, pp. II–II.spa
dc.relation.referencesM. Pauly, M. Gross, and L. P. Kobbelt, “Efficient simplification of point-sampled sur- faces,” in Proceedings of the conference on Visualization’02. IEEE Computer Society, 2002, pp. 163–170.spa
dc.relation.referencesR. B. Rusu, Z. C. Marton, N. Blodow, M. E. Dolha, and M. Beetz, “Functional object mapping of kitchen environments.” in IROS, 2008, pp. 3525–3532spa
dc.relation.referencesShining 3D, AutoScan-DS-EX: Entry Level Dental 3D Scanner, 2023. [Online]. Available: https://www.shining3d.comspa
dc.relation.referencesP. Cignoni, M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, and G. Ranzuglia, “Meshlab: An open-source mesh processing tool,” in Proceedings of the Eurographics Italian Chapter Conference, V. Scarano, R. De Chiara, and U. Erra, Eds. Salerno, Italy: The Eurographics Association, 2008, pp. 129–136.spa
dc.relation.referencesI. Tajik, N. Mushtaq, and M. Khan, “Arch forms among different angle classifications a—study,” Pak. Oral Dent. J., vol. 31, 2011.spa
dc.relation.referencesM. Nouri, S. Asefi, A. Baghban, M. Ahmadvand, and M. Shamsa, “Objective vs. subjec- tive analyses of arch form and preformed archwire selection,” Am. J. Orthod. Dentofac. Orthop., vol. 149, pp. 543–554, 2016.spa
dc.relation.referencesH. Bueller, “Ideal facial relationships and goals,” Facial Plastic Surgery, vol. 34, no. 05, pp. 458–465, 2018.spa
dc.relation.referencesJ.-W. Song, R. Leesungbok, S.-J. Park, S. H. Chang, S.-J. Ahn, and S.-W. Lee, “Analysis of crown size and morphology, and gingival shape in the maxillary anterior dentition in korean young adults,” The Journal of Advanced Prosthodontics, vol. 9, no. 4, p. 315, 2017.spa
dc.relation.referencesJ. D. McCoy, “A consideration of normal arch form and some of the methods of de- termining it,” International Journal of Orthodontia and Oral Surgery (1919), vol. 5, no. 12, pp. 697–723, 1919.spa
dc.relation.referencesS. A. Othman, E. Soh, S. Lim, M. Jamaludin, N. Mohamed, Z. Yusof, L. Shoaib, and N. Hussein, “Comparison of arch form between ethnic malays and malaysian aborigines in peninsular malaysia,” The Korean Journal of Orthodontics, vol. 42, pp. 47–54, 02 2012.spa
dc.relation.references3M, “OrthoForm™ Arch Form Selection,” https://www.3mae.ae/3M/en AE/p/d/ v000360116/, 3M United Arab Emirates, 2024, accessed on 28-March-2024spa
dc.relation.references] “Dental numbering system,” Pressbooks: Oral Surgery for RNs, accessed: Sep. 14, 2024. [Online]. Available: https://pressbooks.pub/oralsurgery4rns/spa
dc.relation.referencesL. R. Dice, “Measures of the amount of ecologic association between species,” Ecology, vol. 26, no. 3, pp. 297–302, 1945. [Online]. Available: http://dx.doi.org/10.2307/1932409spa
dc.relation.referencesH. H. Chang, A. H. Zhuang, D. J. Valentino, and W. C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” NeuroImage, vol. 47, no. 1, pp. 122–135, 2009. [Online]. Available: http: //dx.doi.org/10.1016/j.neuroimage.2009.03.068spa
dc.relation.referencesG. Gerig, M. Jomier, and M. Chakos, Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp. 516–523. [Online]. Available: https://doi.org/10.1007/3-540-45468-3 62spa
dc.relation.referencesA. Fenster and B. Chiu, “Evaluation of Segmentation algorithms for Medical Imaging,” 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 7186–7189, 2005. [Online]. Available: http://ieeexplore.ieee.org/document/1616166/spa
dc.relation.referencesJ. K. Udupa, V. R. LeBlanc, Y. Zhuge, C. Imielinska, H. Schmidt, L. M. Currie, B. E. Hirsch, and J. Woodburn, “A framework for evaluating image segmentation algorithms,” Computerized Medical Imaging and Graphics, vol. 30, no. 2, pp. 75–87, 2006.spa
dc.relation.referencesA. Serna and B. Marcotegui, “Detection, segmentation and classification of 3d urban objects using mathematical morphology and supervised learning,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, pp. 243–255, 2014.spa
dc.relation.referencesA. Hoover, G. Jean-Baptiste, X. Jiang, P. J. Flynn, H. Bunke, D. B. Goldgof, K. Bowyer, D. W. Eggert, A. Fitzgibbon, and R. B. Fisher, “An experimental comparison of range image segmentation algorithms,” IEEE transactions on pattern analysis and machine intelligence, vol. 18, no. 7, pp. 673–689, 1996.spa
dc.relation.referencesJ. Tamayo-Quintero, S. Arboleda-Duque, and J. G´omez-Mendoza, “Semi-automatic teeth segmentation in 3d models of dental casts using a hybrid methodology,” in Emer- ging Trends in Image Processing, Computer Vision and Pattern Recognition. Elsevier, 2015, pp. 433–446.spa
dc.relation.referencesP. Mariaca, A. Garces, N. Trujillo, A. Ramirez, E. Florez, and D. Liz, Manual para realizaci´on de historia cl´ınica odontol´ogica del escolar. Universidad Cooperativa de Colombia, 2007. [Online]. Available: https://books.google.com.co/books?id= 89OZXQM067MCspa
dc.relation.referencesG. Spreitzer, J. Tunnicliffe, and H. Friedrich, “Large wood (lw) 3d accumulation mapping and assessment using structure from motion photogrammetry in the laboratory,” Journal of Hydrology, vol. 581, p. 124430, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0022169419311655spa
dc.relation.referencesA. Serna and B. Marcotegui, “Detection, segmentation and classification of 3d urban objects using mathematical morphology and supervised learning,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 93, 07 2014.spa
dc.relation.referencesL. Avila and I. Kitware, The VTK User’s Guide. Kitware, 2010. [Online]. Available: https://books.google.com.co/books?id=6IxSewAACAAJspa
dc.relation.referencesJ. B. Smith, D. R. Vann, and P. Dodson, “Dental morphology and variation in theropod dinosaurs: implications for the taxonomic identification of isolated teeth,” The Anato- mical Record Part A: Discoveries in Molecular, Cellular, and Evolutionary Biology, vol. 285, no. 2, pp. 699–736, 2005.spa
dc.relation.referencesT. Kronfeld, D. Brunner, and G. Brunnett, “Snake-based segmentation of teeth from virtual dental casts,” Computer-Aided Design and Applications, vol. 7, no. 2, pp. 221– 233, 2010.spa
dc.relation.referencesD. Mairaj, S. D. Wolthusen, and C. Busch, “Teeth segmentation and feature extrac- tion for odontological biometrics,” in 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE, 2010, pp. 323–328.spa
dc.relation.referencesC. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652–660.spa
dc.relation.referencesC. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in neural information processing systems, 2017, pp. 5099–5108.spa
dc.relation.referencesK. Wu, L. Chen, J. Li, and Y. Zhou, “Tooth segmentation on dental meshes using morphologic skeleton,” Computers and Graphics (Pergamon), vol. 38, no. 1, pp. 199– 211, 2014.spa
dc.relation.referencesR. Yanagisawa and S. Omachi, “Extraction of 3d shape of a tooth from dental ct images with region growing method,” in International Workshop on Computational Forensics. Springer, 2010, pp. 68–77.spa
dc.relation.referencesC. Revol and M. Jourlin, “A new minimum variance region growing algorithm for image segmentation,” Pattern Recognition Letters, vol. 18, no. 3, pp. 249–258, 1997.spa
dc.relation.referencesX. Zhou, Y. Gan, J. Xiong, D. Zhang, Q. Zhao, and Z. Xia, “A method for tooth model reconstruction based on integration of multimodal images,” Journal of Healthcare Engineering, vol. 2018, 2018.spa
dc.relation.referencesK. El Emam, L. Mosquera, and R. Hoptroff, Practical Synthetic Data Generation: Ba- lancing Privacy and the Broad Availability of Data. Sebastopol, CA, USA: O’Reilly Media, 2020.spa
dc.relation.referencesO. Huseby, “Use of synthetic health data in prototyping for developing dental implant registry services,” The University of Bergen, 2022.spa
dc.relation.referencesL. Kloski and N. Kloski, Getting Started with 3D printing. books.google.com, 2021. [Online]. Available: https://books.google.com/books?hl=en&lr=&id= rdhyEAAAQBAJ&oi=fnd&pg=PP12&dq=digital+dentistry+3d+dental+modeling+ point+cloud+data+ply+stl+obj+pcd+”cad+cam”+technology&ots=wVIZymMstc& sig=qVtcn5XDoLFgLh1vSu0UEqjN65Mspa
dc.relation.references] S. Barone, A. Paoli et al., “Computer-aided modelling of three-dimensional maxillofacial tissues through multi-modal imaging,” Proceedings, 2013. [Online]. Available: https://journals.sagepub.com/doi/abs/10.1177/0954411912463869spa
dc.relation.referencesJ. Wang, M. Ruilova, and S. Hsieh, “A web-based platform for automated vat photopolymerization additive manufacturing process,” Journal of Advanced Manufacturing Technology, 2022. [Online]. Available: https://link.springer.com/article/ 10.1007/s00170-021-08318-2spa
dc.relation.referencesC. Hagemann, Going the Distance. kclpure.kcl.ac.uk, 2022. [Online]. Available: https://kclpure.kcl.ac.uk/ws/portalfiles/portal/198471391/2023 Hagemann Cathleen 1869404 ethesis.pdfspa
dc.relation.referencesS. Abdul Rehman, S. Rizwan, S. Faisal, and S. Hussain, “Association between intercanine width and mandibular dental arch forms,” J. Coll. Physicians Surg. Pak., vol. 30, pp. 478–480, 2021, pMID: 33866740. [Online]. Available: https://doi.org/10.29271/jcpsp.2021.04.478spa
dc.relation.referencesV. Paulino, V. Paredes, J. Gandia, and R. Cibrian, “Prediction of arch length based on intercanine width,” Eur. J. Orthod., vol. 30, pp. 295–298, 2008.spa
dc.relation.referencesO. Elhiny, M. Elyazied, and G. Salem, “Prediction of arch perimeter based on arch width as a guide for diagnosis and treatment planning,” Bull. Natl. Res. Cent., vol. 45, p. 141, 2021.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc610 - Medicina y salud::617 - Cirugía, medicina regional, odontología, oftalmología, otología, audiologíaspa
dc.subject.proposalDigital dentistryeng
dc.subject.proposalImage processingeng
dc.subject.proposalOrthodonticseng
dc.subject.proposalSegmentation techniqueseng
dc.subject.proposalDental arch shape classificationeng
dc.subject.proposalMachine learningeng
dc.subject.proposal3D Databaseeng
dc.subject.proposalStatistical analysiseng
dc.subject.proposalSoftware toolseng
dc.subject.proposalDentalArcheng
dc.subject.proposalDentiShape3Deng
dc.subject.proposalOdontología digitalspa
dc.subject.proposalProcesamiento de imágenesspa
dc.subject.proposalOrtodonciaspa
dc.subject.proposalTécnicas de segmentaciónspa
dc.subject.proposalClasificación de formas de arco dentaleng
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalBase de datos 3Dspa
dc.subject.proposalAnálisis estadísticospa
dc.subject.proposalHerramientas de Softwarespa
dc.subject.unescoSegmentación de imágenesspa
dc.subject.unescoModelos dentalesspa
dc.titleDiseño de una metodología para el mejoramiento de procesos odontológicos basados en técnicas de segmentación en odontología digitalspa
dc.title.translatedDesign of a methodology for the improvement of dental processes based on image processing in digital dentistryeng
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
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oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.awardtitleAnálisis y diseño de una metodología basada en técnicas de segmentación, caracterización y medición asistida en odontología digitalspa
oaire.fundernameMinCienciasspa
oaire.fundernameCONVOCATORIA 757 DE 2016- DOCTORES NACIONALES DE MINCIENCIASspa

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Doctorado en Ingeniería - Automática