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...
| Main Authors: | , |
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2020
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173 |
| _version_ | 1801706084996481024 |
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| author | Rodríguez-Bastidas, Oscar Vargas-Rosero, Hermes Fabián |
| author_facet | Rodríguez-Bastidas, Oscar Vargas-Rosero, Hermes Fabián |
| author_sort | Rodríguez-Bastidas, Oscar |
| collection | OJS |
| 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. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-10173 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2020 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-101732021-07-13T02:24:15Z Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession Generación de modelos 3D de tumor desde imágenes DICOM, para planificación virtual de su recesión Rodríguez-Bastidas, Oscar Vargas-Rosero, Hermes Fabián 3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad 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. 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. Universidad Pedagógica y Tecnológica de Colombia 2020-04-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf application/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173 10.19053/01211129.v29.n54.2020.10173 Revista Facultad de Ingeniería; Vol. 29 No. 54 (2020): Continuos Publication; e10173 Revista Facultad de Ingeniería; Vol. 29 Núm. 54 (2020): Publicación Continua; e10173 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9130 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173/9598 N.A. N.A. Copyright (c) 2020 Oscar Rodríguez-Bastidas, Hermes Fabián Vargas-Rosero, M.Sc. |
| spellingShingle | 3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad Rodríguez-Bastidas, Oscar Vargas-Rosero, Hermes Fabián Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
| title | Generation of 3D Tumor Models from DICOM Images for Virtual Planning of its Recession |
| title_alt | Generación de modelos 3D de tumor desde imágenes DICOM, para planificación virtual de su recesión |
| 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_short | 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 |
| topic | 3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad |
| topic_facet | 3D mesh 3D model image segmentation k-means medical images usability imágenes médicas k-means malla 3D modelo 3D segmentación de imágenes usabilidad |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/10173 |
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