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...

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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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network_acronym_str REPOUPTC2
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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
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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