Title page for ETD etd-12182009-121450

Type of Document Dissertation
Author Rajagopalan, Vidya
URN etd-12182009-121450
Title Increasing DBM Reliability using Distribution Independent Tests and Information Fusion Techniques
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Wyatt, Christopher L. Committee Chair
Kim, Inyoung Committee Member
Mili, Lamine M. Committee Member
Wang, Ge Committee Member
Wang, Yue J. Committee Member
  • information fusion
  • deformation based morphometry
  • MRI
  • image registration
  • distribution independent tests
Date of Defense 2009-10-06
Availability unrestricted
In deformation based morphometry (DBM) group-wise differences in brain structure are measured

using deformable registration and some form of statistical test. However, it is known that DBM

results are sensitive to both the registration method and statistical test used. Given the lack of an

objective model of group variation it has been difficult to determine the extent of the influence

of registration implementation or contraints on DBM analysis. In this thesis, we use registration

methods with varying levels of theoretic similarity to study the influence of registration mechanics

on DBM results. We show that because of the extent of the influence of registration mechanics

on DBM results, analysis of changes should always be made with a thorough understanding

of the registration method used. We also show that minor variations in registration methods can

lead to large changes in DBM results. When using DBM, it would be imprudent to use only one

registration method to draw any conclusions about the variations being studied. In order to provide

a more complete representation of inter-group changes, we propose a method for combining

multiple registration methods using Dempster-Shafer evidence theory to produce belief maps of

categorical changes between groups. We show that the Dempster-Shafer combination produces a

unique and easy to interpret belief map of regional changes between and within groups without the

complications associated with hypothesis testing.

Another, often confounding, element of DBM is the parametric hypothesis test used to specify

voxels undergoing significant change between the two groups. The accuracy and reliability of

these tests are contingent on a number of fundamental assumptions made about the distribution of

the data used in the tests. Many DBM studies often overlook these assumptions and fail to verify

their validity for the data being tested. This raises many doubts about the credibility of the results

from such tests. In this thesis, we propose to perform statistical analysis on DBM data using nonparametric,

distribution independent hypothesis tests. With no data distributional assumptions,

these tests provide both increased flexibility and reliability of DBM statistical analysis

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