SemVisM: semantic visualizer for medical image

Luis Landaeta; Alexandra La Cruz; Alexander Baranya; María-Esther Vidal

doi:10.1117/12.2073826

28 January 2015 SemVisM: semantic visualizer for medical image

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

Author Affiliations +

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928712 (2015) https://doi.org/10.1117/12.2073826
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

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

Citation Download Citation

Luis Landaeta, Alexandra La Cruz, Alexander Baranya, and María-Esther Vidal "SemVisM: semantic visualizer for medical image", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928712 (28 January 2015); https://doi.org/10.1117/12.2073826

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