Convex geometry for rapid tissue classification in MRI

Erick Wong; Craig Jones

doi:10.1117/12.467119

9 May 2002 Convex geometry for rapid tissue classification in MRI

Erick Wong, Craig Jones

Proceedings Volume 4684, Medical Imaging 2002: Image Processing; (2002) https://doi.org/10.1117/12.467119
Event: Medical Imaging 2002, 2002, San Diego, California, United States

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.

Citation Download Citation

Erick Wong and Craig Jones "Convex geometry for rapid tissue classification in MRI", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); https://doi.org/10.1117/12.467119

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