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Proceedings Paper

Medical image segmentation using high-performance computer clusters
Author(s): Ruben Cardenes-Almeida; Juan Ruiz-Alzola; Ron Kikinis; Simon Keith Warfield
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Paper 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.

Paper Details

Date Published: 28 May 2001
PDF: 9 pages
Proc. SPIE 4319, Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures, (28 May 2001); doi: 10.1117/12.428041
Show Author Affiliations
Ruben Cardenes-Almeida, Univ. de Las Palmas de Gran Canaria (Spain)
Juan Ruiz-Alzola, Univ. de Las Palmas de Gran Canaria and Brigham & Women's Hospital/Harvard Medical School (United States)
Ron Kikinis, Brigham & Women's Hospital/Harvard Medical School (United States)
Simon Keith Warfield, Brigham & Women's Hospital/Harvard Medical School (United States)


Published in SPIE Proceedings Vol. 4319:
Medical Imaging 2001: Visualization, Display, and Image-Guided Procedures
Seong Ki Mun, Editor(s)

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