Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47464
Title: Medical image segmentation using high performance computer clusters
Authors: Almeida, R. C.
Ruiz-Alzola, J. 
Kikinis, R.
Warfield, S. K.
UNESCO Clasification: 3314 Tecnología médica
Keywords: Image segmentation
Medical imaging
Magnetic resonance imaging
3D modeling
Brain, et al
Issue Date: 2001
Publisher: 0277-786X
Journal: Proceedings of SPIE - The International Society for Optical Engineering 
Conference: Medical Imaging 2001 Conference 
Abstract: 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
Source: Proceedings of SPIE - The International Society for Optical Engineering[ISSN 0277-786X],v. 4319, p. 88-91
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