Abstract
A novel CUDA based high performance parallel voxel growing algorithm to segment 3D CT pulmonary volumes with GPU Acceleration is introduced in this paper. The optimal parameters for segmentation is dynamically iterative adjusted based on the statistical information about previous segmented regions. To avoid the disadvantage of leaking during segmentation with the conventional voxel-growing based methods, it adopts a process to mutually utilize segment results between both of lateral lung leaves, which in turn benefits the discriminative segmentation on left and right lung leaves. Experiments show that the algorithms obtain accurate results with a speed about 10-20 times faster than the traditional methods on CPU, which imply that this algorithm is potentially valid for future clinical diagnosis applications.
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Zhai, W., Yang, F., Song, Y., Zhao, Y., Wang, H. (2010). CUDA Based High Performance Adaptive 3D Voxel Growing for Lung CT Segmentation. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_2
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DOI: https://doi.org/10.1007/978-3-642-15615-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15614-4
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