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Proceedings Paper

Supercomputing in medical science
Author(s): William J. Hanson; H. Joseph Myers; Ralph Bernstein; Robert L. DeLapaz
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Paper Abstract

Supercomputer facilities have been applied to a problem in numerically intensive medical image processing. Magnetic Resonance Imaging (MRI) data was converted into a useful information product. The motivation for this work is the "information overload" that radiologists currently experience with the overwhelming amount of data that MRI scans produce. The work was encouraged by past success in using image processing on earth observation satellite programs. The objectives of this work were to determine if the source data, multiple MRI echos, could be converted into one tissue map and to assess the computational requirements. We found that vectorizing of numerically intensive kernels reduces CPU use by a factor of 2-3 times. Our initial experience with the application of fuzzy and ISODATA clustering analysis provides data dimension reduction, improved tissue specificity, and provides a more quantitative diagnostic tool for the radiologist.

Paper Details

Date Published: 1 May 1990
PDF: 6 pages
Proc. SPIE 1245, Biomedical Image Processing, (1 May 1990); doi: 10.1117/12.19558
Show Author Affiliations
William J. Hanson, IBM Corp. (United States)
H. Joseph Myers, IBM Corp. (United States)
Ralph Bernstein, IBM Corp. (United States)
Robert L. DeLapaz, Stanford Univ. School of Medicine (United States)

Published in SPIE Proceedings Vol. 1245:
Biomedical Image Processing
Alan Conrad Bovik; William E. Higgins, Editor(s)

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