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

Aaron Hagan, Ye Zhao
Kent State University
Advances in Visual Computing, Lecture Notes in Computer Science, 2009, Volume 5876/2009, 960-969


   title={Parallel 3D Image Segmentation of Large Data Sets on a GPU Cluster},

   author={Hagan, A. and Zhao, Y.},

   journal={Advances in Visual Computing},





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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.
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