A streaming narrow-band algorithm: interactive computation and visualization of level sets

Lefohn, Aaron; Kniss, Joe; Hansen, Charles; Whitaker, Ross T.

doi:10.1109/tvcg.2004.2

Cited by 89 publications

(78 citation statements)

References 33 publications

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“…To the best of our knowledge, the only memory-adaptive model for the level set representation on the GPU is due to Lefohn et al [18]. In this method, the domain is decomposed into small 2D tiles, of which only the tiles with non-zero derivatives are stored on the GPU.…”

Section: B Level Set Gpu Methodsmentioning

confidence: 99%

“…Similar to the methods by Lefohn et al [18] and Bridson [4], the Sorted Tile List (STL) method [37] divides the domain into fixed-size tiles, such that each tile represents a part of the domain of the level set function φ . Tiles outside the narrowband are discarded.…”

Section: Sparse Cpu Methodsmentioning

confidence: 99%

“…6. Since during the simulation, φ should be maintained close to a distance transform, all STL methods use for this benchmark a simplified PDE (as frequently used in image segmentation) with an additional rescaling-speed term sgn(φ )(1−|∇φ |) [18], which enforces |∇φ | = 1. The open-source DT-Grid performs every time step one iteration of the so-called reinitialization PDE, φ t = sgn(φ )(1 − |∇φ |).…”

Section: A Efficiency: Comparison To Other Sparse Cpu Methodsmentioning

confidence: 99%

“…Our fast GPU method further improves upon the work by Lefohn et al [18] to bring the use of level set methods into the realm of interactive simulations. Specifically, the main contributions of this paper are:…”

Section: Introductionmentioning

confidence: 94%

“…Our GPU sparse level-set method is similar to that of Lefohn et al [18], in that it uses small, fixed-size blocks, i.e., tiles, to represent the narrow band around the interface. However, in contrast to Lefohn's method, we do not need to store a map of the complete domain nor have we to maintain a list of neighbours for each tile.…”

Section: Comparison To Previous Approachesmentioning

confidence: 99%

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Fast Sparse Level Sets on Graphics Hardware

Jalba

Laan

Roerdink

2013

IEEE Trans. Visual. Comput. Graphics

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Abstract-The level set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically-based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multi-resolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level set method.

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Section: B Level Set Gpu Methodsmentioning

confidence: 99%

Section: Sparse Cpu Methodsmentioning

confidence: 99%

Section: A Efficiency: Comparison To Other Sparse Cpu Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: Comparison To Previous Approachesmentioning

confidence: 99%

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Fast Sparse Level Sets on Graphics Hardware

Jalba

Laan

Roerdink

2013

IEEE Trans. Visual. Comput. Graphics

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From MRI to anatomical simulation of the hip joint

Assassi

Charbonnier

Schmid

et al. 2008

Computer Animation & Virtual

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This paper describes a methodology for the simulation of musculoskeletal disorders. Our clinical study is related to osteoarthritis (OA) of the hip, a pathogenesis possibly due to impingements. These bone collisions lead to abnormal joint mechanics which is characterized by contact pressure and stress distribution upon the joint cartilages. The proposed methodology combines different approaches from modeling to simulation. The simulation is based on patient-specific anatomical models, where acquisition modalities are noninvasive and flexible. Based on static magnetic resonance imaging (MRI) data, a discrete deformable models method is used for modeling the organs of the musculoskeletal system. Femoroacetabular movements are estimated using an optical motion capture system and are validated by a dynamic MRI analysis. To achieve accurate deformations, techniques to generate volumetric meshes are developed based on the medial axis (MA) information. Finally, a computationally efficient fast functional joint model is used to simulate the mechanical behavior of the soft tissues. The goal of such a simulation is to allow the investigation of the relevant contact and cartilages deformation under movement, which can be useful for diagnosis, pre-or post-operative planning and training. This will benefit further developments in surgical techniques and minimally invasive procedures.

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