An immersive virtual environment for DT-MRI volume visualization applications: a case study

Zhang, Bo; Demiralp, Tamer; Keefe,; DaSilva,; Laidlaw,; Greenberg,; Basser,; Pierpaoli,; Chiocca,; Deisboeck,

doi:10.1109/visual.2001.964545

Cited by 73 publications

(42 citation statements)

References 11 publications

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“…Understanding the data: It takes a lot of understanding to get data in the right shape so that you can use visualization as part of data analysis [8]. For example, if the data comes from social media content, you need to know who the user is in a general sense -such as a customer using a particular set of products -and understand what it is you're trying to visualize out of the data.…”

Section: Challenges Faced By Visualizationmentioning

confidence: 99%

M3: Model data representation using virtual reality [Model Qualify, Model Verify, Model Validity]

2017

IJIET

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Abstract-Researchers have argued that perceptual issues are important in determining what makes an effective visualization, but generally only provide descriptive guidelines for transforming perceptual theory into practical designs.In order to bridge the gap between theory and practice in a more rigorous way, a computational model of the primary visual cortex is used to explore the perception of data visualizations. A method is presented for automatically evaluating and optimizing data visualizations for an analytical task using a computational model of human vision. The method relies on a neural network simulation of early perceptual processing in the retina and visual cortex. The neural activity resulting from viewing information visualization is simulated and evaluated to produce metrics of visualization effectiveness for analytical tasks. Visualization optimization is achieved by applying these effectiveness metrics as the utility function in a hill-climbing algorithm. This method is applied to the evaluation and optimization of two visualization types 2D flow visualizations and node-link graph visualizations. The computational perceptual model is applied to various visual representations of flow fields evaluated using the advection task of Laidlaw et al. [1]. The predictive power of the model is examined by comparing its performance to that of human subjects on the advection task using four flow visualization types. The results show the same overall pattern for humans and the model. In both cases, the best performance was obtained from visualizations containing aligned visual edges. Flow visualization optimization is done using both streaklet-based and pixel-based visualization parameterizations. An emergent property of the streaklet-based optimization is head-to-tail streaklet alignment, the pixel-based parameterization results in a LIC-like result. Here we propose M3 Visualization for mathematical approach for improve HCI performance in statistical representation.

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Section: Challenges Faced By Visualizationmentioning

confidence: 99%

M3: Model data representation using virtual reality [Model Qualify, Model Verify, Model Validity]

2017

IJIET

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“…None of the people who participated in these experiments had former experience of working with stereo cursor, but many of them had one time or more experience of watching stereo contents. Moreover, some of them were already familiar with some 3D input devices such as space navigator [10] and a few with some line cursors used to point to a 3D location within the CAVE VR systems [25].…”

Section: User Evaluationmentioning

confidence: 99%

Stereo 3D Mouse Cursor: A Method for Interaction with 3D Objects in a Stereoscopic Virtual 3D Space

Azari¹,

Cheng²,

Basu

2010

International Journal of Digital Multimedia Broadcasting

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We introduce a different approach of applying stereoscopy principles to implement a virtual 3D pointing technique called stereo 3D mouse cursor (S3D-Cursor) based on two or more views of an ordinary mouse cursor. The basics of such an idea have already been applied as a by-product of some stereo-based visualization applications with usually less attention to its strengths or weaknesses as a generic alternative of its 2D counterpart in stereoscopic 3D space. Here, we examine if such an idea satisfies all or the main expected requirements of an abstract 3D cursor. Moreover, we analyze its accuracy and evaluate the applicability of this approach in terms of different efficiency factors. For this purpose, we have adapted a real-time point-based rendering software called QSplat to a multiview rendering version named QSplatMV. We have implemented the S3D-Cursor on top of this new application and developed a simple editing toolset for manipulating the virtual 3D objects. Our user evaluation results suggest the effectiveness of the approach in terms of detection accuracy and user satisfaction compared to using an ordinary mouse cursor on a conventional 2D screen.

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“…We developed stream tube and stream surface geometric models [48] that relate to the interesting structures in the tissue while representing the DTI data with a focus on studying white-matter structures to understand changes due to a number of pathologies and for pre-operative planning for tumor surgery. In the VE, geometric models representing the white matter structures and planes showing sections of anatomical scan data generally are kept stationary above a virtual table.…”

Section: Diffusion Mri Visualizationmentioning

confidence: 99%

Virtual Reality-Based Interactive Scientific Visualization Environments

LaViola¹,

Prabhat²,

Forsberg³

et al. 2008

Trends in Interactive Visualization

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Immersive Virtual Reality (IVR) has some production applications (e.g., in vehicle design and psychiatric treatment), but it is often viewed as an expensive, over-hyped technology with insufficient and/or unproven benefit over conventional desktop systems. With steady research progress at many institutions and advances in hardware and software technologies, immersive scientific visualization is a third application area in which IVR is having a positive impact and is beginning to attract more attention from the scientific community. At Brown University, we have been researching immersive scientific visualization systems by developing interactive systems with domain scientists in a variety of fields, including archaeology, biology, computational fluid dynamics (CFD), and geoscience. Anecdotal and formal evaluations show the benefits range from mild to very significant -benefits include speeding up and providing broader analysis, greater spatial understanding, enabling new types of exploration, helping in undergraduate as well as graduate courses, and debugging data acquisition methods. In this chapter, we present a number of these systems developed at Brown University and discuss the results of our experimental findings on the efficacy of IVR-based interactive visualization.Keywords Immersive virtual reality , Scientific visualization , Interactive visualization , 3D interfaces , Surround screen display , Virtual environments. IntroductionScientists studying the physical world frequently need to analyze complex data sets (multidimensional, multivariate, time-varying) and models. Our group of interactive visualization researchers subscribes to the theory that in order to best understand such data we must "impedance match" display and interaction devices as best we can with human motor and sensory capabilities, in a task-dependent way. We feel E.

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An immersive virtual environment for DT-MRI volume visualization applications: a case study

Cited by 73 publications

References 11 publications

M3: Model data representation using virtual reality [Model Qualify, Model Verify, Model Validity]

M3: Model data representation using virtual reality [Model Qualify, Model Verify, Model Validity]

Stereo 3D Mouse Cursor: A Method for Interaction with 3D Objects in a Stereoscopic Virtual 3D Space

Virtual Reality-Based Interactive Scientific Visualization Environments

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