Convex geometry for rapid tissue classification in MRI

Erick B. Wong, Craig K. Jones

Research output: Contribution to journalArticlepeer-review

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 1.5 GHz 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.

Original languageEnglish (US)
Pages (from-to)1524-1530
Number of pages7
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4684 III
DOIs
StatePublished - Jan 1 2002
Externally publishedYes

Keywords

  • Classification
  • Computational geometry
  • Image processing
  • Linear combinations
  • MRI
  • Magnetic resonance imaging
  • Segmentation
  • Vector decomposition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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