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Proceedings Paper

Convex geometry for rapid tissue classification in MRI
Author(s): Erick Wong; Craig Jones
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Paper Abstract

We propose an efficient computational engine for solving linear combination problems that arise in tissue classification on dual-echo MRI data. In 2D feature space, each pure tissue class is represented by a central point, together with a circle representing a noise tolerance. A given unclassified voxel can be approximated by a linear combination of these pure tissue classes. With more than three tissue classes, multiple combinations can represent the same point, thus heuristics are employed to resolve this ambiguity. An optimised implementation is capable of classifying 1 million voxels per second into four tissue types on a 1.5GHz Pentium 4 machine. Used within a region-growing application, it is found to be at least as robust and over 10 times faster than numerical optimization and linear programming methods.

Paper Details

Date Published: 9 May 2002
PDF: 7 pages
Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467119
Show Author Affiliations
Erick Wong, Univ. of British Columbia (Canada)
Craig Jones, Univ. of British Columbia (Canada)

Published in SPIE Proceedings Vol. 4684:
Medical Imaging 2002: Image Processing
Milan Sonka; J. Michael Fitzpatrick, Editor(s)

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