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http://hdl.handle.net/10553/47464
| Título: | Medical image segmentation using high performance computer clusters | Autores/as: | Almeida, R. C. Ruiz-Alzola, J. Kikinis, R. Warfield, S. K. |
Clasificación UNESCO: | 3314 Tecnología médica | Palabras clave: | Image segmentation Medical imaging Magnetic resonance imaging 3D modeling Brain, et al. |
Fecha de publicación: | 2001 | Editor/a: | 0277-786X | Publicación seriada: | Proceedings of SPIE - The International Society for Optical Engineering | Conferencia: | Medical Imaging 2001 Conference | Resumen: | A statistical classification algorithm, for MRI segmentation, based on the k Nearest Neighbor rule (kNN) has been implemented with Message Passing Interface (MPI) by partitioning the dataset into similar sized subvolumes and delivering each part to one processor inside a cluster. We have tested the algorithm in two different CPU architectures (SPARC and Intel) and four different configurations including a Beowulf cluster, two Sun clusters and a symmetric multiprocessor. The experiments provide a good speedup in all the cases and show a very good performance/price ratio in the PC-Linux cluster. We present results using a three channel, high resolution original dataset in times less than two minutes in the best cases and we use the segmented maps to make clinically relevant 3D visualizations in interactive times. | URI: | http://hdl.handle.net/10553/47464 | ISSN: | 0277-786X | DOI: | 10.1117/12.428041 | Fuente: | Proceedings of SPIE - The International Society for Optical Engineering[ISSN 0277-786X],v. 4319, p. 88-91 |
| Colección: | Artículos |
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