Moment curves

Patel, Daniel; Haidacher, Martin; Balabanian, Jean-Paul; Gröller, Eduard

doi:10.1109/pacificvis.2009.4906857

Cited by 27 publications

(16 citation statements)

References 10 publications

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“…Examples include local intensity structures such as sheets and blobs [106], statistical moments [113,114] and moment curves [88], as well as estimations of local mean and standard deviations [33,102]. Local histogram correlation [75,76] and low-high (LH) histograms [107] are also proposed.…”

Section: Classification Specificity and Multiple Dimensionsmentioning

confidence: 99%

Medical Volume Visualization Beyond Single Voxel Values

Lindholm¹

2014

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Acknowledgments AbstractMedical visualization involves many complex decisions for both the user and the imaging algorithms. This thesis aims to improve medical volume visualization through a series of technical contributions to aid such decision processes. Improvements are achieved by using more data, beyond single voxels, in the associated visual analyses.Simultaneous visualization of multiple data sources and different data formats is rapidly becoming a necessity. This is due to both the growing number of data producing image acquisition techniques as well as the increase in geometric data representations that can be created. Maintaining high rendering performance under these circumstances is challenging, but necessary, to support an exploratory visualization process. This thesis proposes two algorithms to address this challenge: a multi-volume approach that applies binary-space partitioning to solve painters' algorithm geometrically and a rendering algorithm for hybrid data that improves the management of the available graphics memory.Additional information for decision support is often derived from the captured image data. Classification techniques, in particular, often utilize secondary information sources or neighborhood analysis as means to improve specificity. One example is a proposed algorithm that improves visualization of blood vessels by automatically optimizing visualization parameters based on observed vesselness. This thesis also proposes algorithms involving neighborhood analysis, with a particular focus on domain specific classification knowledge provided by the user. One algorithm provides the ability to semantically state spatial relations between tissues based on encoded material information. Another algorithm improves the representation of discrete features by integrating the users' knowledge in the reconstruction step of the visualization pipeline.Many of the methods proposed in this thesis can also be applied to other domains, but are all described here in the context of medical volume visualization as most of the research has been performed within this field.v Populärvetenskaplig SammanfattningMedicinsk visualisering involverar en mängd komplexa beslut, både för användaren och för bildsystemet. I många fall handlar dessa beslut om vilken information som skall visas och hur den skall visas. Utfallen av dessa beslut kan potentiellt förbättras ju mer data som inkluderas i beslutsprocessen bortom enskilda voxlar. Den här avhandlingen avser att förbättra och effektivisera medicinsk volumsvisualisering genom en rad tekniska forskningsbidrag.Samtidig visualisering av multipla datakällor och olika dataformat är snabbt på väg att bli en nödvändighet. Dels på grund av det växande antalet dataproducerande bildmetoder, men även på grund av det stigande antalet geometriska representationer som kan skapas. Att producera snabba effektiva renderingar under dessa förutsättningar är svårt, men också nödvändigt för att stödja den ofta explorativa visualiseringsprocessen. Ytterligare information för besluts...

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Section: Classification Specificity and Multiple Dimensionsmentioning

confidence: 99%

Medical Volume Visualization Beyond Single Voxel Values

Lindholm¹

2014

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“…The visualization community also proposed various TF design by exploiting curvatures [18], relative sizes [6], and ambient occlusion [7], in order to separate and present more features. Statistical properties can also be extracted as new dimensions for TF design [12], [26].…”

Section: Related Workmentioning

confidence: 99%

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

Guo

Yuan

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel transfer function interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate (dimension) and the MDS plots project features. They are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, direct sketching on the volume rendered images has been implemented to probe and edit features. With our system, users can interactively analyze multivariate volumetric data sets by navigating and exploring feature spaces in unified PCP and MDS plots. To further support large scale multivariate volume data visualization and analysis, Scalable Pivot MDS (SPMDS), parallel adaptive continuous PCP rendering, as well as parallel rendering techniques are developed and integrated into our visualization system. Our experiments show that the system is effective in multivariate volume data visualization and its performance is highly scalable for data sets with different sizes and number of variates.

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“…In comparison to our work, they used a fixed neighborhood size to estimate the variance. In our previous work (Patel et al [21]) we used statistical properties to manually classify materials for differently sized neighborhood regions. In this work we extract the statistical properties for the best suited neighborhood size semi-automatically.…”

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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Moment curves

Cited by 27 publications

References 10 publications

Medical Volume Visualization Beyond Single Voxel Values

Medical Volume Visualization Beyond Single Voxel Values

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

Volume visualization based on statistical transfer-function spaces

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