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Conference Papers Year : 2016

Eidolon: Visualization and Computational Framework for Multi-Modal Biomedical Data Analysis

Abstract

Biomedical research, combining multi-modal image and geometry data, presents unique challenges for data visualization, processing , and quantitative analysis. Medical imaging provides rich information , from anatomical to deformation, but extracting this to a coherent picture across image modalities with preserved quality is not trivial. Addressing these challenges and integrating visualization with image and quantitative analysis results in Eidolon, a platform which can adapt to rapidly changing research workflows. In this paper we outline Eidolon, a software environment aimed at addressing these challenges, and discuss the novel integration of visualization and analysis components. These capabilities are demonstrated through the example of cardiac strain analysis , showing the Eidolon supports and enhances the workflow.
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Dates and versions

hal-01346357 , version 1 (18-07-2016)

Identifiers

  • HAL Id : hal-01346357 , version 1

Cite

Eric Kerfoot, Lauren Fovargue, Simone Rivolo, Wenzhe Shi, Daniel Rueckert, et al.. Eidolon: Visualization and Computational Framework for Multi-Modal Biomedical Data Analysis. 7th International Conference on Medical Imaging and Augmented Reality (MIAR 2016), Aug 2016, Berne, Switzerland. ⟨hal-01346357⟩
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