Comparison of GPU-based and CPU-based algorithms for determining the minimum distance between a CUSA scalpel and blood vessels

In this paper, we design a GPU-based algorithm for determining the minimum distance from the tip of CUSA scalpel to the closest blood vessel. The calculation time of GPU-based algorithm is to be O(1), which does not depend on shape complexity of blood vessels, i.e., their patch number. On the other hand, calculation time of CPU-based algorithm is to be O(n). Even if we use several kinds of hierarchical structures in positioning such as sorting in XYZ axes, calculation complexity of CPU-based algorithms is to be O(log(n)) at the best. As contrasted with this, when each STL is always converted into rectangular parallelepiped group in Z-buffer of GPU, we should accept quantization errors in XY-plane and Z-axis. Furthermore, so as to design parallel processing in GPU, we should omit all exclusive controls. As a result, distance precision and scanning range calculated by the GPU-based algorithm become worse than those done by the CPU-based algorithm. However, the precision (0.5mm) and range (0〜50mm) are fully accepted by the doctor’s request.

[1]  Hiroshi Noborio,et al.  Fast interference check method using octree representation , 1988, Adv. Robotics.

[2]  Gino van den Bergen.  Efficient Collision Detection of Complex Deformable Models using AABB Trees , 1997, J. Graphics, GPU, & Game Tools.

[3]  Yen-Wei Chen,et al.  Segmentation of Liver in Low-Contrast Images Using K-Means Clustering and Geodesic Active Contour Algorithms , 2013, IEICE Trans. Inf. Syst..

[4]  Ming C. Lin,et al.  A fast algorithm for incremental distance calculation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[5]  Sean Quinlan,et al.  Efficient distance computation between non-convex objects , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[6]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[7]  S. Sathiya Keerthi,et al.  A fast procedure for computing the distance between complex objects in three-dimensional space , 1988, IEEE J. Robotics Autom..