Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession
Medical images are essential for diagnosis, planning of surgery and evolution of pathology. The advances in technology have developed new techniques to obtain digital images with more details, in return this has also led to disadvantages, such as: the analysis of large volumes of information, long t...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Pedagógica y Tecnológica de Colombia
- Repositorio:
- RiUPTC: Repositorio Institucional UPTC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uptc.edu.co:001/14253
- Acceso en línea:
- https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173
https://repositorio.uptc.edu.co/handle/001/14253
- Palabra clave:
- 3D mesh
3D model
image segmentation
k-means
medical images
usability
imágenes médicas
k-means
malla 3D
modelo 3D
segmentación de imágenes
usabilidad
- Rights
- License
- Copyright (c) 2020 Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc.
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repository_id_str |
|
dc.title.en-US.fl_str_mv |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
dc.title.es-ES.fl_str_mv |
Generación de modelos 3D de tumor desde imágenes DICOM, para planificación virtual de su recesión |
title |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
spellingShingle |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession 3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad |
title_short |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
title_full |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
title_fullStr |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
title_full_unstemmed |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
title_sort |
Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
dc.subject.en-US.fl_str_mv |
3D mesh 3D model image segmentation k-means medical images usability |
topic |
3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad |
dc.subject.es-ES.fl_str_mv |
imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad |
description |
Medical images are essential for diagnosis, planning of surgery and evolution of pathology. The advances in technology have developed new techniques to obtain digital images with more details, in return this has also led to disadvantages, such as: the analysis of large volumes of information, long time to determine an affected region and difficulty in defining the malignant tissue for its later extirpation, among the most relevant. This article presents an image segmentation strategy and the optimization of a method for generating three-dimensional models. A prototype was implemented in which it was possible to evaluate the segmentation algorithms and 3D reconstruction technique, allowing to visualize the tumor model from different points of view through virtual reality. In this investigation, we evaluate the computational cost and user experience, the parameters selected in terms of computational cost are the time and consumption of RAM, we used 140 MRI images each with dimensions 260x320 pixel, and as a result, we obtained an approximate time of 37.16s and consumption in RAM of 1.3GB. Another experiment carried out is the segmentation and reconstruction of a tumor, this model is formed by a three-dimensional mesh made up of 151 vertices and 318 faces. Finally, we evaluate the application, with a usability test applied to a sample of 20 people with different areas of knowledge. The results show that the graphics presented by the software are pleasant, they also show that the application is intuitive and easy to use. Additionally, it is mentioned that it helps improve the understanding of medical images. |
publishDate |
2020 |
dc.date.accessioned.none.fl_str_mv |
2024-07-05T19:11:52Z |
dc.date.available.none.fl_str_mv |
2024-07-05T19:11:52Z |
dc.date.none.fl_str_mv |
2020-04-01 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.coarversion.spa.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a219 |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173 10.19053/01211129.v29.n54.2020.10173 |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.uptc.edu.co/handle/001/14253 |
url |
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173 https://repositorio.uptc.edu.co/handle/001/14253 |
identifier_str_mv |
10.19053/01211129.v29.n54.2020.10173 |
dc.language.none.fl_str_mv |
eng |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9130 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9598 |
dc.rights.en-US.fl_str_mv |
Copyright (c) 2020 Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc. |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf136 |
rights_invalid_str_mv |
Copyright (c) 2020 Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc. http://purl.org/coar/access_right/c_abf136 http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
application/pdf application/xml |
dc.coverage.en-US.fl_str_mv |
N.A. |
dc.coverage.es-ES.fl_str_mv |
N.A. |
dc.publisher.en-US.fl_str_mv |
Universidad Pedagógica y Tecnológica de Colombia |
dc.source.en-US.fl_str_mv |
Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e10173 |
dc.source.es-ES.fl_str_mv |
Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e10173 |
dc.source.none.fl_str_mv |
2357-5328 0121-1129 |
institution |
Universidad Pedagógica y Tecnológica de Colombia |
repository.name.fl_str_mv |
Repositorio Institucional UPTC |
repository.mail.fl_str_mv |
repositorio.uptc@uptc.edu.co |
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spelling |
2020-04-012024-07-05T19:11:52Z2024-07-05T19:11:52Zhttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/1017310.19053/01211129.v29.n54.2020.10173https://repositorio.uptc.edu.co/handle/001/14253Medical images are essential for diagnosis, planning of surgery and evolution of pathology. The advances in technology have developed new techniques to obtain digital images with more details, in return this has also led to disadvantages, such as: the analysis of large volumes of information, long time to determine an affected region and difficulty in defining the malignant tissue for its later extirpation, among the most relevant. This article presents an image segmentation strategy and the optimization of a method for generating three-dimensional models. A prototype was implemented in which it was possible to evaluate the segmentation algorithms and 3D reconstruction technique, allowing to visualize the tumor model from different points of view through virtual reality. In this investigation, we evaluate the computational cost and user experience, the parameters selected in terms of computational cost are the time and consumption of RAM, we used 140 MRI images each with dimensions 260x320 pixel, and as a result, we obtained an approximate time of 37.16s and consumption in RAM of 1.3GB. Another experiment carried out is the segmentation and reconstruction of a tumor, this model is formed by a three-dimensional mesh made up of 151 vertices and 318 faces. Finally, we evaluate the application, with a usability test applied to a sample of 20 people with different areas of knowledge. The results show that the graphics presented by the software are pleasant, they also show that the application is intuitive and easy to use. Additionally, it is mentioned that it helps improve the understanding of medical images.Las imágenes médicas son imprescindibles para la realización del diagnóstico, planificación de cirugía y evolución de la patología. El avance de la tecnología ha desarrollado nuevas técnicas para obtener imágenes digitales con más detalles, esto a su vez ha llevado a tener desventajas, entre ellas: el análisis de grandes volúmenes de información, tiempo prolongado para determinar una región afectada y dificultad para definir el tejido maligno para su posterior extirpación, entre las más relevantes. Este artículo presenta una estrategia de segmentación de imágenes y la optimización de un método de generación de modelos tridimensionales. Se implementó un prototipo en el que se logró evaluar los algoritmos de segmentación y técnica de reconstrucción 3D permitiendo visualizar el modelo del tumor desde diferentes puntos de vista mediante realidad virtual. En esta investigación, se evalúa el costo computacional y la experiencia del usuario, los parámetros seleccionados en términos de costo computacional son el tiempo y el consumo de RAM, se utilizaron 140 imágenes MRI cada una de ellas con dimensiones de 260x320 píxeles, y como resultado, se obtuvo un tiempo aproximado de 37.16s y el consumo de memoria RAM es de 1.3GB. Otro experimento llevado a cabo es la segmentación y reconstrucción de un tumor, este modelo está formado por una malla tridimensional que contiene 151 vértices y 318 caras. Finalmente, se evalúa la aplicación con una prueba de usabilidad aplicada a una muestra de 20 personas con diferentes áreas de conocimiento, los resultados muestran que los gráficos presentados por el software son agradables, también se evidencia que el software es intuitivo y fácil de usar. También mencionan que ayuda a mejorar la compresión de imágenes médicas.application/pdfapplication/xmlengengUniversidad Pedagógica y Tecnológica de Colombiahttps://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9130https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9598Copyright (c) 2020 Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc.http://purl.org/coar/access_right/c_abf136http://purl.org/coar/access_right/c_abf2Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e10173Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e101732357-53280121-11293D mesh3D modelimage segmentationk-meansmedical imagesusabilityimágenes médicask-meansmalla 3Dmodelo 3Dsegmentación de imágenesusabilidadGeneration of 3D Tumor Models from DICOM Images for Virtual Planning of its RecessionGeneración de modelos 3D de tumor desde imágenes DICOM, para planificación virtual de su recesióninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a219http://purl.org/coar/version/c_970fb48d4fbd8a85N.A.N.A.Rodríguez-Bastidas, OscarVargas-Rosero, Hermes Fabián001/14253oai:repositorio.uptc.edu.co:001/142532025-07-18 11:53:37.446metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCrepositorio.uptc@uptc.edu.co |