Identificador persistente para citar o vincular este elemento: 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
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.