Signed distance transform using graphics hardware

Sigg, Christian; Peikert, Ronald; Groß, Markus

doi:10.1109/visual.2003.1250358

Cited by 87 publications

(70 citation statements)

References 13 publications

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“…Hoff et al (1999) were one of the first to use graphics hardware to accelerate the computation of Voronoi diagrams. Sigg et al (2003) implemented the signed distance transform, using OpenGL's ARB fragment program. This work was improved and extended by Sud et al (2004), who introduced culling to decrease the number of distance calculations, and by Hsieh and Tai (2005), who focused on the construction of Voronoi diagrams.…”

Section: Distance Transformsmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Section: Distance Transformsmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

344

198

View full text Add to dashboard Cite

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“…The polyhedron containing points closer to the respective feature are then scan converted to compute the distances. Sigg et al used a graphic hardware to scan convert the characteristics for a faster implementation [30].…”

Section: Voxel Based Renderingmentioning

confidence: 99%

Distance Field based Haptic Rendering of Scattered Oriented Points

Sreeni¹

2013

IJCA

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This work is aimed at rendering of an object described by a scattered, oriented point set data without explicitly finding the bounding surface. We propose a proxy based rendering technique on a distance field based representation of the object in a regular 3D grid of voxels. Our method initially finds a 3D indicator function in the grid of voxels from the available point set data through sphere packing. The indicator function is further used to find the implicit potential function (distance field) in the voxel grid by iteratively moving the bounding surface of the object inward till all the voxels are covered. Our algorithm uses a gradient descent based minimization of the distance between HIP and proxy during the penetration of HIP in the object. The rendering algorithm interpolates the distance at the proxy point from the neighboring voxels in order to find the gradient for the purpose of moving the proxy. We experimented on a large set of scattered point set data and could effectively render them using a three degree of freedom haptic device.

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“…The GPU has also been used to compute 3D Voronoi diagrams [27], [30], [31], [32], [33]. Weber et al [34] adopted the raster scan method in the geometry image to compute the Voronoi diagram using the geodesic distance on a surface.…”

Section: Gpu Algorithmsmentioning

confidence: 99%

GPU-Assisted Computation of Centroidal Voronoi Tessellation

Rong

Liu

Wang

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-Centroidal Voronoi tessellations (CVT) are widely used in computational science and engineering. The most commonly used method is Lloyd's method, and recently the L-BFGS method is shown to be faster than Lloyd's method for computing the CVT. However, these methods run on the CPU and are still too slow for many practical applications. We present techniques to implement these methods on the GPU for computing the CVT on 2D planes and on surfaces, and demonstrate significant speedup of these GPU-based methods over their CPU counterparts. For CVT computation on a surface, we use a geometry image stored in the GPU to represent the surface for computing the Voronoi diagram on the surface. In our implementation a new technique is proposed for parallel regional reduction on the GPU for evaluating integrals over Voronoi cells.

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Signed distance transform using graphics hardware

Cited by 87 publications

References 13 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Distance Field based Haptic Rendering of Scattered Oriented Points

GPU-Assisted Computation of Centroidal Voronoi Tessellation

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