1 June 1991 Tissue identification in MR images by adaptive cluster analysis
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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.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dan Gutfinger, Dan Gutfinger, Efrat M. Hertzberg, Efrat M. Hertzberg, Thomas Tolxdorff, Thomas Tolxdorff, Fred Greensite, Fred Greensite, Jack Sklansky, Jack Sklansky, } "Tissue identification in MR images by adaptive cluster analysis", Proc. SPIE 1445, Medical Imaging V: Image Processing, (1 June 1991); doi: 10.1117/12.45226; https://doi.org/10.1117/12.45226

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