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Proceedings Paper

Tissue identification in MR images by adaptive cluster analysis
Author(s): Dan Gutfinger; Efrat M. Hertzberg; Thomas Tolxdorff; Fred Greensite; Jack Sklansky
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Paper Abstract

We describe how adaptive cluster analysis and a linear model of tissue-mixing can achieve improved identification of tissues in MR images, with less reliance on human interaction. Our technique consists of two successive phases: a supervised training phase, which involves a small amount of human interaction; and an unsupervised training phase, which implements adaptive clustering. Two versions of unsupervised training are described. In the first version, which is comparable to earlier methods, no attempt is made to deal with the partial volume problem, whereas in the second version additional steps are taken to identify partial volume voxels and to estimate the tissue composition of such voxels. The reliability and accuracy of each of these versions are evaluated. We describe the results of comparative tests of our algorithms on a software phantom, MR images of a physical phantom, and in vivo MR images of human brains. These results indicate that accounting for partial volumes can improve the reliability of tissue identification.

Paper Details

Date Published: 1 June 1991
PDF: 9 pages
Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); doi: 10.1117/12.45226
Show Author Affiliations
Dan Gutfinger, Univ. of California/Irvine (United States)
Efrat M. Hertzberg, Univ. of California/Irvine (United States)
Thomas Tolxdorff, RWTH Aachen (Germany)
Fred Greensite, Univ. of California/Irvine (United States)
Jack Sklansky, Univ. of California/Irvine (United States)


Published in SPIE Proceedings Vol. 1445:
Medical Imaging V: Image Processing
Murray H. Loew, Editor(s)

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