Detección automática de hueso en imágenes médicas 3D a partir de segmentación con mallado anisótropo adaptativo
[EN] In Finite Element analysis of medical images, obtained by CT Scan and magnetic resonance, there is a problem in the calculus because of the same color scale of different body parts, for example, sometimes the cancellous bone and the muscles have the same color value, involving indeterminations...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/89998 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/89998 |
| Access Level: | acceso abierto |
| Palabra clave: | Segmentation Galerkin Finite element method Automatic bone detection Patient specific Elementos finitos Mallado anisótropo Segmentación Cartesian grid Mallado anisótropo adaptativo Detecció automàtica d’os Mètode dels elements finits Segmentació Mallat anisòtrop adaptatiu INGENIERIA MECANICA Máster Universitario en Ingeniería Mecánica-Màster Universitari en Enginyeria Mecànica |
| Sumario: | [EN] In Finite Element analysis of medical images, obtained by CT Scan and magnetic resonance, there is a problem in the calculus because of the same color scale of different body parts, for example, sometimes the cancellous bone and the muscles have the same color value, involving indeterminations assigning material properties to the different tissues. This master’s thesis tries to obtain an application that automatically detects the bone, or other body parts, in medical images for its subsequent analysis using the cartesian grid Finite Element Method (cgFEM). In the methodology developed there is a preprocessing, where the images are prepared for the segmentation. Afterwards, a segmentation is done, obtained by a two-dimensional anisotropic mesh adaptation, developed in the Mathematics Department of the Polytechnic in Milano. Finally, there is a postprocessing, where a logical matrix is created, using the mesh in the segmentation, that distinguish between those parts of the image that are useful for its analysis with cgFEM and those that are not. The tests done with different medical images shows satisfactory results, like those detailed in the report, achieving the thesis’s goal. |
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