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ISVC
2009
Springer

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

10 years 9 months ago
Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster
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.
Aaron Hagan, Ye Zhao
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ISVC
Authors Aaron Hagan, Ye Zhao
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