Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster

Authors:
Aaron Hagan;Ye Zhao
Affiliations:
Kent State University,;Kent State University,
Venue:
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Year:
2009

Citing 4
Cited 2

Shape Modeling with Front Propagation: A Level Set Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Lattice Boltzmann Models for Anisotropic Diffusion of Images

Journal of Mathematical Imaging and Vision
Lattice Boltzmann based PDE solver on the GPU

The Visual Computer: International Journal of Computer Graphics
A streaming narrow-band algorithm: interactive computation and visualization of level sets

IEEE Transactions on Visualization and Computer Graphics

Image multi-thresholding by combining the lattice Boltzmann model and a localized level set algorithm

Neurocomputing
Power and Performance Management of GPUs Based Cluster

International Journal of Cloud Applications and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an inherent parallel scheme for 3D image segmentation of large volume data on a GPU cluster. This method originates from an extended Lattice Boltzmann Model (LBM), and provides a new numerical solution for solving the level set equation. As a local, explicit and parallel scheme, our method lends itself to several favorable features: (1) Very easy to implement with the core program only requiring a few lines of code; (2) Implicit computation of curvatures; (3) Flexible control of generating smooth segmentation results; (4) Strong amenability to parallel computing, especially on low-cost, powerful graphics hardware (GPU). The parallel computational scheme is well suited for cluster computing, leading to a good solution for segmenting very large data sets.