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Proceedings Paper

SemVisM: semantic visualizer for medical image
Author(s): Luis Landaeta; Alexandra La Cruz; Alexander Baranya; María-Esther Vidal
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Paper Abstract

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.1

Paper Details

Date Published: 28 January 2015
PDF: 6 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928712 (28 January 2015); doi: 10.1117/12.2073826
Show Author Affiliations
Luis Landaeta, Univ. Simón Bolívar (Venezuela)
Alexandra La Cruz, Univ. de Cuenca (Ecuador)
Alexander Baranya, Univ. Simón Bolívar (Venezuela)
María-Esther Vidal, Univ. Simón Bolívar (Venezuela)

Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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