GPU-accelerated Parallel 3D Image Thinning

The skeletons of the objects in 3D images can be extracted by using 3D image thinning. The application of 3D image thinning for image analysis is hampered by its considerable computation time. By employing the graphics processing unit (GPU), which has tremendous powerful computing power at an incomparable performance-to-cost ratio, the calculation of 3D image thinning can be accelerated. In this paper, we proposed a parallel implementation approach on GPU for the 3D 12-subiteration image thinning algorithm, in which object voxels of 3D image are assigned to threads based on the characteristic of sparse 3D image data. The performance of our approach is analyzed with different image sizes, the ratio of object voxels and the number of thread grids on GPU. The performance of the traditional threads assignment strategy and new threads assignment strategy are compared to show that the proposed approach is more efficient.