Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Guo, Hanqi; He, Xiao; Yuan, Xiaoru

doi:10.1109/tvcg.2012.80

Cited by 40 publications

(3 citation statements)

References 41 publications

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“…One common approach is to explore the value domain of multimodal medical images. Several techniques [ 29 – 31 ] based on multidimensional diagrams facilitate selections of specific value ranges of each data attribute. However, value domain approaches are unfamiliar to medical experts.…”

Section: Fundamental Medical Image Visualization Techniquesmentioning

confidence: 99%

A Review of Three-Dimensional Medical Image Visualization

et al. 2022

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Importance. Medical images are essential for modern medicine and an important research subject in visualization. However, medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques that could increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Our paper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medical experts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for various medical imaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatial perception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on a procedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes free software tools for different modalities of medical images designed for various purposes, including visualization, analysis, and segmentation, and it provides respective Internet links. Conclusions. Visualization techniques are a useful tool for medical experts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques given the medical problem and modalities of associated medical images. We summarize fundamental techniques and readily available visualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contribute to the joint effort of the medical and visualization communities to advance precision medicine.

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Section: Fundamental Medical Image Visualization Techniquesmentioning

confidence: 99%

A Review of Three-Dimensional Medical Image Visualization

et al. 2022

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“…The user first frames the ROI using the touch tablet and then uses the tablet as a magical lens to target at with a VR controller and raycasting. Transfer functions can also extract ROIs [KIL∗03, GXY12]. Wiebel et al [WVFH12] selects a ROI based on the 3D color and opacity field as defined by the transfer function, the virtual camera transformation, and the user's 2D input.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Touch/Tangible Spatial Selection in Augmented Reality

Sereno

Gosset

Besançon

et al. 2022

Computer Graphics Forum

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We study tangible touch tablets combined with Augmented Reality Head‐Mounted Displays (AR‐HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR‐HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR‐HMDs merge the visualization, interaction, and the users' physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets' touch displays provide their own visualization and interaction spaces, separated from those of the AR‐HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR‐HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.

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“…An example is shown in Figure 17. Guo et al [GXY12] later extend their previous work to combine PC plots with multidimensional scaling to avoid the need to adjust parameters for each dimension separately. Liu and Shen [LS16] have proposed to use association analysis for multivariate scientific data sets to generate a parallel coordinate plot.…”

Section: Parallel Coordinatesmentioning

confidence: 99%

State of the Art in Transfer Functions for Direct Volume Rendering

Ljung

Krüger

Gröller

et al. 2016

Computer Graphics Forum

106

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A central topic in scientific visualization is the transfer function (TF) for volume rendering. The TF serves a fundamental role in translating scalar and multivariate data into color and opacity to express and reveal the relevant features present in the data studied. Beyond this core functionality, TFs also serve as a tool for encoding and utilizing domain knowledge and as an expression for visual design of material appearances. TFs also enable interactive volumetric exploration of complex data. The purpose of this state-of-the-art report (STAR) is to provide an overview of research into the various aspects of TFs, which lead to interpretation of the underlying data through the use of meaningful visual representations. The STAR classifies TF research into the following aspects: dimensionality, derived attributes, aggregated attributes, rendering aspects, automation, and user interfaces. The STAR concludes with some interesting research challenges that form the basis of an agenda for the development of next generation TF tools and methodologies.

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Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Cited by 40 publications

References 41 publications

A Review of Three-Dimensional Medical Image Visualization

A Review of Three-Dimensional Medical Image Visualization

Hybrid Touch/Tangible Spatial Selection in Augmented Reality

State of the Art in Transfer Functions for Direct Volume Rendering

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