An evaluation of reconstruction filters for volume rendering

Marschner, Steve; Lobb, Richard J.

doi:10.1109/visual.1994.346331

Cited by 174 publications

(210 citation statements)

References 14 publications

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“…A frequently used approach to the evaluation of interpolation kernels is to compare the spatial and spectral properties of these kernels to those of the sinc function, either by discussing their low-frequency band-pass and the high-frequency suppression capabilities [29,41], or by using such metrics as "sampling and reconstruction blur" [39,47], "smoothing", "post-aliasing", or "overshoot" [30], "truncation error", or "non-sinc error" [28], to mention but a few. These approaches are based on the fundamental assumption that in all cases, the sinc function is the optimal interpolation kernel.…”

Section: Discussion Of Evaluation Strategiesmentioning

confidence: 99%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

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Abstract-Interpolation is required in a variety of medical image processing applications. Although many interpolation techniques are known from the literature, evaluations of these techniques for the specific task of applying geometrical transformations to medical images are still lacking. In this paper we present such an evaluation. We consider convolution-based interpolation methods and rigid transformations (rotations and translations). A large number of sincapproximating kernels are evaluated, including piecewise polynomial kernels and a large number of windowed sinc kernels, with spatial supports ranging from two to ten grid intervals. In the evaluation we use images from a wide variety of medical image modalities. The results show that spline interpolation is to be preferred over all other methods, both for its accuracy and its relatively low computational cost.

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Section: Discussion Of Evaluation Strategiesmentioning

confidence: 99%

Quantitative evaluation of convolution-based methods for medical image interpolation

Meijering

Niessen

Viergever

2001

Medical Image Analysis

254

165

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“…Other research has analyzed the capabilities of both separable and the more computationally expensive nonseparable filters (for example, [24], [2]). Neumann et al propose a method they call 4D linear regression for gradient estimation, which approximates the density function in a local neighborhood with a 3D regression hyperplane [28].…”

Section: Featuresmentioning

confidence: 99%

Volume illustration: nonphotorealistic rendering of volume models

Rheingans

Ebert

2001

IEEE Trans. Visual. Comput. Graphics

124

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AbstractÐAccurately and automatically conveying the structure of a volume model is a problem not fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images which may match the appearance of translucent materials in nature, but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance, but generally require substantial hand tuning for each new data set in order to be effective. We introduce the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using nonphotorealistic rendering techniques. Since features to be enhanced are defined on the basis of local volume characteristics rather than volume sample value, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features and the addition of illumination effects.

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“…We performed our study using the Marschner-Lobb (ML) dataset [3], which has almost uniform frequency content all the way up to the Nyquist limit (see Figure 2 for an iso-surface volume rendering). Measurements were taken for an angular range of 0-45° at 10° increments, and the projections were compared with the simulated projection obtained at the same angle in a numerical sense, via its RMS error.…”

Section: Resultsmentioning

confidence: 99%

Optimal sampling lattices for high-fidelity CT reconstruction

Mueller

2009

2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC)

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-We show that the familiar Cartesian lattice, while convenient for signal processing and representation, is suboptimal when it comes to signal fidelity. We explore various applications of optimal sampling lattices, such as the Hexagonal and Body Centered Cartesian (BCC) lattices for 3D Computed Tomographic (CT) Reconstruction, both in terms of the (2D) detector and the (3D) reconstructed object. We find that BCC lattices compare favorably with CC lattices for both CT data acquisition and reconstruction. For example they increase the recovery and detectability of small features, such as small tumors in the brain.

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An evaluation of reconstruction filters for volume rendering

Abstract: Abstract

Cited by 174 publications

References 14 publications

Quantitative evaluation of convolution-based methods for medical image interpolation

Quantitative evaluation of convolution-based methods for medical image interpolation

Volume illustration: nonphotorealistic rendering of volume models

Optimal sampling lattices for high-fidelity CT reconstruction

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