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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eu2m4gixpvbj3bgkrmcfu4ezdi" style="color: black;">Computers & graphics</a>
Centerline extraction is important in a variety of visualization applications including shape analysis, geometry processing, and virtual endoscopy. Centerlines allow accurate measurements of length along winding tubular structures, assist automatic virtual navigation, and provide a path-planning system to control the movement and orientation of a virtual camera. However, efficiently computing centerlines with the desired accuracy has been a major challenge. Existing centerline methods are<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.cag.2014.02.003">doi:10.1016/j.cag.2014.02.003</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iq3e6ypz7bcpfd2duwvsnt4qvm">fatcat:iq3e6ypz7bcpfd2duwvsnt4qvm</a> </span>
more »... not fast enough or not accurate enough for interactive application to complex 3D shapes. Some methods based on distance mapping are accurate, but these are sequential algorithms which have limited performance when running on the CPU. To our knowledge, there is no accurate parallel centerline algorithm that can take advantage of modern many-core parallel computing resources, such as GPUs, to perform automatic centerline extraction from large data volumes at interactive speed and with high accuracy. In this paper, we present a new parallel centerline extraction algorithm suitable for implementation on a GPU to produce highly accurate, 26-connected, one-voxel-thick centerlines at interactive speed. The resulting centerlines are as accurate as those produced by a state-of-the-art sequential CPU method  , while being computed hundreds of times faster. Applications to fly-through path planning and virtual endoscopy are discussed. Experimental results demonstrating centeredness, robustness and efficiency are presented.
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