Robust unsupervised tissue classification in MR images

Dzung L. Pham, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A general framework for performing robust, unsupervised tissue classification in magnetic resonance images is presented. Tissue classification is formulated as an estimation problem based on an imaging model. Prior models are used within the estimation problem to compensate for noise and intensity inhomogeneity artifacts. From this framework, approaches based on K-means clustering, clustering via the expectation-maximization algorithm, and fuzzy clustering can be derived. The performance of the different types of approaches are evaluated using both simulated and real neuroimaging data.

Original languageEnglish (US)
Title of host publication2004 2nd IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationMacro to Nano
Pages109-112
Number of pages4
StatePublished - Dec 1 2004
Event2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano - Arlington, VA, United States
Duration: Apr 15 2004Apr 18 2004

Publication series

Name2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Volume1

Other

Other2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano
Country/TerritoryUnited States
CityArlington, VA
Period4/15/044/18/04

ASJC Scopus subject areas

  • Engineering(all)

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