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Bayesian model selection and classification: application to brain tissues through T distribution

University of British Columbia

A Bayesian procedure for model selection, parameter estimation and classification, using models of non-orthogonal basis functions, is applied to the problem of T2 decay rate distributions in brain tissues. The feasibility of generating reliable synthetic images of tissue-classified pixels is examined. The work determines, for the first time, the Bayesian probability of existence of short (5-15ms) T2 component in the brain tissues, and found it to be higher than 99% for all white matter tissues and higher than 80% for all gray matter tissues except Cortical Gray . The probability of having no more than three components of decaying exponents in the Ti distributions of the brain tissues, is found to be higher than 90% for all the tissues. We arrive to these findings through the use of models which are parameterized by highly coupled parameters, and the use of multi-dimensional search in the space of these models.

Thesis/Dissertation

application/pdf

2009-01-19

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http://hdl.handle.net/2429/3796

Physics

University of British Columbia

UBCV

DSpace