Local Histograms for Design of Transfer Functions in Direct Volume Rendering

Lundström, Claes; Ljung, Patric; Ynnerman, Anders

doi:10.1109/tvcg.2006.100

Cited by 108 publications

(59 citation statements)

References 17 publications

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“…Multidimensional histograms have been used by Kindlmann et al [4], [5], [6] and by Kniss et al [7], [8] to exploit relationships between isovalues and gradients. In a further variation, local histograms were proposed by Lundström et al [9] to allow users to examine sub-regions of the volume in greater detail.…”

Section: Related Workmentioning

confidence: 99%

Integrating Isosurface Statistics and Histograms

Duffy

Carr

Möller

2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-Many data sets are sampled on regular lattices in two, three or more dimensions, and recent work has shown that statistical properties of these data sets must take into account the continuity of the underlying physical phenomena. However, the effects of quantization on the statistics have not yet been accounted for. This paper therefore reconciles the previous papers to the underlying mathematical theory, develops a mathematical model of quantized statistics of continuous functions, and proves convergence of geometric approximations to continuous statistics for regular sampling lattices. In addition, the computational cost of various approaches is considered, and recommendations made about when to use each type of statistic.

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Section: Related Workmentioning

confidence: 99%

Integrating Isosurface Statistics and Histograms

Duffy

Carr

Möller

2013

IEEE Trans. Visual. Comput. Graphics

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“…surfaces) in multivariate data that are not able to be isolated with simple 1D transfer functions. Finally, Lundstrom et al [30] combine user domain knowledge, in the form of local histogram criteria, into a certainty-based classification strategy to create transfer functions for direct volume rendering. They apply their strategy on magnetic resonance data and show that their constructed transfer functions clearly detect and separate important tissues of interest, e.g.…”

Section: Distributions In Visualizationmentioning

confidence: 99%

An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Gosink

Garth²,

Anderson

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.

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“…Lundström et al [18] introduced a method to classify different tissues by the local histograms in the neighborhood around a sample point. Caban and Rheingans [2] used textural properties to differentiate between materials, possibly with similar data values.…”

Section: Related Workmentioning

confidence: 99%

Volume visualization based on statistical transfer-function spaces

Haidacher

Patel

Brückner

et al. 2010

2010 IEEE Pacific Visualization Symposium (PacificVis)

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It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transferfunction space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transferfunction space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.

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Local Histograms for Design of Transfer Functions in Direct Volume Rendering

Cited by 108 publications

References 17 publications

Integrating Isosurface Statistics and Histograms

Integrating Isosurface Statistics and Histograms

An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Volume visualization based on statistical transfer-function spaces

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