SemVisM: Semantic visualizer for medical image

Luis Landaeta, Alexandra La Cruz, Alexander Baranya, María Esther Vidal

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojada10th International Symposium on Medical Information Processing and Analysis
EditoresEduardo Romero, Natasha Lepore
EditorialSPIE
ISBN (versión digital)9781628413625
DOI
EstadoPublicada - 2015
Evento10th International Symposium on Medical Information Processing and Analysis - Cartagena de Indias, Colombia
Duración: 14 oct 201416 oct 2014

Serie de la publicación

NombreProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volumen9287
ISSN (versión impresa)1605-7422

Conferencia

Conferencia10th International Symposium on Medical Information Processing and Analysis
País/TerritorioColombia
CiudadCartagena de Indias
Período14/10/1416/10/14

Huella

Profundice en los temas de investigación de 'SemVisM: Semantic visualizer for medical image'. En conjunto forman una huella única.

Citar esto