SemVisM: Semantic visualizer for medical image
SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically combining renderi...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Ecuador |
| Institución: | Universidad de Cuenca |
| Repositorio: | Repositorio Universidad de Cuenca |
| OAI Identifier: | oai:dspace.ucuenca.edu.ec:123456789/29155 |
| Acceso en línea: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923067790&doi=10.1117%2f12.2073826&partnerID=40&md5=e9ee9432fc7ddc69371931e44a30a334 http://dspace.ucuenca.edu.ec/handle/123456789/29155 |
| Access Level: | acceso abierto |
| Palabra clave: | Medical Ontologies Semantic Annotation Semantic Segmentation Semantic Visualization |
| Sumario: | SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE. |
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