Medical image rendering and description driven by semantic annotations

Alexandra La Cruz, Alexander Baranya, Maria Esther Vidal

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

5 Citas (Scopus)

Resumen

Image-driven medical applications can aid medical experts to visualize tissues and organs, and thus facilitate the task of identifying anomalies and tumors. However, to ensure reliable results, regions of the image that enclose the organs or tissues of interest have to be precisely visualized. Volume rendering is a technique for visualizing volumetric data by computing a 2D projection of the image. Traditionally, volume rendering generates a semi-transparent image, enhancing the description of the area of interest to be visualized. Particularly during the visualization of medical images, identification of areas of interest depends on existing characterizations of the tissues, their corresponding intensities, and the medical image acquisition modality, e.g., Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). However, a precise classification of a tissue requires specialized segmentation processes to distinguish neighboring tissues that share overlapped intensities. Semantic annotations of ontologies such as, RadLex and the Foundational Model of Anatomy (FMA), conceptually allow the annotation of areas that enclose particular tissues. This may impact on the segmentation process or the volume rendering quality. We survey state-of-the-art approaches that support medical image discovery and visualization based on semantic annotations, and show the benefits of semantically encoding medical images for volume rendering. As a proof of concept, we present ANISE (an ANatomIc SEmantic annotator) a framework for the semantic annotation of medical images. Finally, we describe the improvements achieved by ANISE during the rendering of a benchmark of medical images, enhancing segmented part of the organs and tissues that comprise the studied images.

Idioma originalInglés
Título de la publicación alojadaResource Discovery - 5th International Workshop, RED 2012, Co-located with the 9th Extended Semantic Web Conference, ESWC 2012, Revised Selected Papers
EditorialSpringer Verlag
Páginas123-149
Número de páginas27
ISBN (versión impresa)9783642452628
DOI
EstadoPublicada - 2013
Evento5th International Workshop on Resource Discovery, RED 2012 - Co-located with the 9th Extended Semantic Web Conference, ESWC 2012 - Heraklion, Grecia
Duración: 27 may 201227 may 2012

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8194 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia5th International Workshop on Resource Discovery, RED 2012 - Co-located with the 9th Extended Semantic Web Conference, ESWC 2012
País/TerritorioGrecia
CiudadHeraklion
Período27/05/1227/05/12

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