MedVis: A Real-Time Immersive Visualization Environment for the Exploration of Medical Volumetric Data

Shen, Rui; Boulanger, Pierre; Noga, Michelle

doi:10.1109/medivis.2008.10

Cited by 15 publications

(11 citation statements)

References 86 publications

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“…Given a volume rendering system with VR support (cf. [37,65]), the technical implementation of Deadeye can be done in the following way. For simplicity, let us assume that Deadeye should be achieved by removing the target for the right eye.…”

Section: Methodsmentioning

confidence: 99%

“…In such a case, our technique would not require any additional hardware and would perform out of the box. Furthermore, due to the availability nowadays of consumer VR equipment, visualization research increasingly employs virtual environments as a target scenario [24,37,44,51,63,65], and having more complex visualizations in VR triggers the need for suitable highlighting approaches.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Krekhov

Cmentowski

Waschk

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Left: Our experiments in VR with homogeneous and heterogeneous distractors, as we investigate the preattentiveness and robustness of Deadeye in such scenarios. Right: We demonstrate and evaluate volume rendering in VR as a possible real-world application scenario for our technique. Abstract-Visualizations rely on highlighting to attract and guide our attention. To make an object of interest stand out independently from a number of distractors, the underlying visual cue, e.g., color, has to be preattentive. In our prior work, we introduced Deadeye as an instantly recognizable highlighting technique that works by rendering the target object for one eye only. In contrast to prior approaches, Deadeye excels by not modifying any visual properties of the target. However, in the case of 2D visualizations, the method requires an additional setup to allow dichoptic presentation, which is a considerable drawback. As a follow-up to requests from the community, this paper explores Deadeye as a highlighting technique for 3D visualizations, because such stereoscopic scenarios support dichoptic presentation out of the box. Deadeye suppresses binocular disparities for the target object, so we cannot assume the applicability of our technique as a given fact. With this motivation, the paper presents quantitative evaluations of Deadeye in VR, including configurations with multiple heterogeneous distractors as an important robustness challenge. After confirming the preserved preattentiveness (all average accuracies above 90 %) under such real-world conditions, we explore VR volume rendering as an example application scenario for Deadeye. We depict a possible workflow for integrating our technique, conduct an exploratory survey to demonstrate benefits and limitations, and finally provide related design implications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Krekhov

Cmentowski

Waschk

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

show abstract

“…This fact (and the progress in GPU performance) allows for good VR experiences on commodity hardware. Immersive visualisation has been previously suggested for molecular visualisation [Stone et al, 2010], medical volumetric data [Shen et al, 2008], for dentistry [Shimabukuro and Minghim, 1998], and computational fluid dynamics [ uam et al, 2015]. More relevant to our approach is the visualisation of the inside of large arterial blood vessels .…”

Section: Related Work On Immersive Visualisationmentioning

confidence: 99%

Three Dimensional Reconstruction

Ikeuchi¹

2014

Computer Vision

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GPU implementation with tandem simplification [Ulrich et al., 2014a, Chapter 4] or a module from 3D Slicer [Fedorov et al., 2012, Kikinis et al., 2014]. The meshes are further processed. In most cases we at least heal and remesh the representation using PolyMender [Ju, 2004], then use Taubin smoothing to obtain a nicer surface. After some further processing (e.g., removal of very small unconnected components or colouring) the mesh is ready for quality control, rendering, and evaluation by experts.

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“…Additionally, they also include ubiquitous computing principles for context-aware interaction with mobile devices. Shen et al (2008) discuss their system which works with stereoscopic displays and uses projectors to provide an immersive experience in environments such as the CAVE.…”

Section: Introductionmentioning

confidence: 99%

Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

Joshi

Scheinost

Okuda³

et al. 2011

Neuroinform

210

163

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Developing both graphical and command-line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software-BioImage Suite (bioimagesuite.org).

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MedVis: A Real-Time Immersive Visualization Environment for the Exploration of Medical Volumetric Data

Cited by 15 publications

References 86 publications

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Deadeye Visualization Revisited: Investigation of Preattentiveness and Applicability in Virtual Environments

Three Dimensional Reconstruction

Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

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