Paralelização em GPU da segmentação vascular com extração de Centerlines por Height Ridges
The vascular segmentation is important in diagnosing vascular diseases like stroke and 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 with height ridges, which uses the intensity as fe...
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| Formato: | Dissertação |
| Idioma: | por |
| Publicado em: |
Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/jspui/handle/123456789/18035 |
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| Resumo: | The vascular segmentation is important in diagnosing vascular diseases like stroke
and 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 with
height ridges, which uses the intensity as features for segmentation. This process can
take from seconds to minutes, depending on the current technology employed. In order
to accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002]
we have adapted it to run in parallel using CUDA architecture. The performance of the
segmentation method running on GPU is compared to both the same method running
on CPU and the original Aylward s method running also in CPU. The improvemente of
the new method over the original one is twofold: the starting point for the segmentation
process is not a single point in the blood vessel but a volume, thereby making it easier for
the user to segment a region of interest, and; the overall gain method was 873 times faster
running on GPU and 150 times more fast running on the CPU than the original CPU in
Aylward |
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