Paralelização em GPU da segmentação vascular com extração de Centerlines por Height Ridges

摘要

The vascular segmentation is important in diagnosing vascular diseases like strokeand is hampered by noise in the image and very thin vessels that can pass unnoticed.One way to accomplish the segmentation is extracting the centerline of the vessel withheight ridges, which uses the intensity as features for segmentation. This process cantake from seconds to minutes, depending on the current technology employed. In orderto accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002]we have adapted it to run in parallel using CUDA architecture. The performance of thesegmentation method running on GPU is compared to both the same method runningon CPU and the original Aylward s method running also in CPU. The improvemente ofthe new method over the original one is twofold: the starting point for the segmentationprocess is not a single point in the blood vessel but a volume, thereby making it easier forthe user to segment a region of interest, and; the overall gain method was 873 times fasterrunning on GPU and 150 times more fast running on the CPU than the original CPU inAylward