Uncertainty visualization in HARDI based on ensembles of ODFs

Jiao, Fangxiang; Phillips, Jeff M.; Gur, Yaniv; Johnson, Chris R.

doi:10.1109/pacificvis.2012.6183591

Cited by 28 publications

(14 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first set of methods display the data as a glyph representation [35, 41], which indicates the fiber directional or fiber crossing uncertainty at a given location. The other set of approaches track fibers under uncertain conditions, giving either a color map for confidence [11] or an envelop of potential fiber routes [37].…”

Section: Tensor Fieldsmentioning

confidence: 99%

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Potter

Rosen

Johnson

2012

IFIP Advances in Information and Communication Technology

Self Cite

148

View full text Add to dashboard Cite

Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of disciplines. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.

show abstract

Section: Tensor Fieldsmentioning

confidence: 99%

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Potter

Rosen

Johnson

2012

IFIP Advances in Information and Communication Technology

Self Cite

148

View full text Add to dashboard Cite

show abstract

“…Hlawatsch et al [37] adopted the metaphor of radar to represent the directions of the flow by angles, where the information of time steps was encoded by the radius in spherical co-ordinates. A fiber orientation distribution function (ODF) glyph [38] has been proposed as an accurate expression for uncertainty. Jarema et al [39] proposed a lobular glyph, in which the vector probability density functions are mapped into the shape and orientation of the lobular.…”

Section: Vector Field Visualizationmentioning

confidence: 99%

Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field

Ren

et al. 2020

IJGI

View full text Add to dashboard Cite

Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way to compute the uncertainty. However, existing metrics are not accurate enough or are sensitive to outliers; thus, a comprehensive tool for the further analysis of the uncertainty of transport patterns is required. In this paper, we propose a novel framework for quantifying and analyzing the uncertainty of an ensemble vector field. Based on the classical edit distance on real sequence (EDR) method, a robust and accurate metric was proposed to measure the pathline uncertainty. Considering the spatial continuity, we computed the transport variance of the neighborhood of a location, and evaluated the uncertainty correlation between each location and its neighborhood by using the local Moran’s I. Based on the proposed uncertainty measurements, a visual analysis system called UP-Vis (uncertainty pathline visualization) was developed to interactively explore the uncertainty. It provides an overview of the uncertainty and supports detailed exploration of transport patterns at a selected location, and allows for the comparison of transport patterns between a location and its neighborhood. Through pathline clustering, the major trends of the ensemble pathline at a location were extracted. Moreover, a glyph was designed to intuitively display the transport direction and diverging degree of each cluster. For the uncertainty analysis of the neighborhood, a comparison view was designed to compare the transport patterns between a location and its neighborhood in detail. A synthetic data set and weather simulation data set were used in our experiments. The evaluation and case studies demonstrated that the proposed framework can measure the uncertainty effectively and help users to comprehensively explore uncertainty transport patterns.

show abstract

“…Van Almsick [45] defined a new 3D glyph to depict the ODF and implemented it in GPU. Jiao [22] presented a method for constructing a shape inclusion probability function from a series of estimated diffusion shapes for uncertainty analysis. Shattuck et al [40] developed a mesh polygon on a sphere to obtain the ODF though this method requires complex computation and is time-consuming.…”

Section: Related Workmentioning

confidence: 99%

Visual exploration of HARDI fibers with probabilistic tracking

Liang

Wang

Zhang

et al. 2016

Information Sciences

View full text Add to dashboard Cite

High angular resolution diffusion imaging (HARDI) is an effective method for characterizing complex neural fiber paths in the human brain. However, visualizing and analyzing the fibers is often challenging because of the complexity of the fiber orientation distribution function used to describe the crossing, kissing, and fanning fibers. In this paper, we propose a novel visual analytics approach to study brain fiber paths that allows users to explore fiber bundles to reveal the probability of fiber paths using a new visual classification method. First, we use a spherical deconvolution model for diffusion estimation and a Bayesian theorem for fiber tractography. Second, each fiber is subjected to a cluster analysis using pixel-based visual encoding. This result is shown in a pixel-based visual representation where each pixel bar maps opacity, color, and length to the probability, direction, and length of a fiber. Fiber bundles can then be acquired via a two-step classification routine that uses DBSCAN to group fibers based on similarities. Then the user can further refine fiber bundle selection using probabilistic information from the pixel bars. Therefore, the proposed approach shows not only the shape but also the confidence of the fiber paths. We demonstrate the resulting HARDI fiber bundles and compare a brain with tumor and normal brain using our system. Experiments and an empirical user study verify the effectiveness of our approach.

show abstract

Uncertainty visualization in HARDI based on ensembles of ODFs

Cited by 28 publications

References 53 publications

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches

Uncertainty Visualization of Transport Variance in a Time-Varying Ensemble Vector Field

Visual exploration of HARDI fibers with probabilistic tracking

Contact Info

Product

Resources

About