Document Type

Thesis

Date of Degree

Fall 2009

Degree Name

MS (Master of Science)

Degree In

Biomedical Engineering

First Advisor

Joseph M. Reinhardt

Second Advisor

Hans J. Johnson

Abstract

A method for simultaneously segmenting multiple anatomical brain structures from multi-modal MR images has been developed. An artificial neural network (ANN) was trained from a set of feature vectors created by a combination of high-resolution registration methods, atlas based spatial probability distributions, and a training set of 16 expert traced data sets. A set of feature vectors were adapted to increase performance of ANN segmentation; 1) a modified spatial location for structural symmetry of human brain, 2) neighbors along the priors' descent for directional consistency, and 3) candidate vectors based on the priors for the segmentation of multiple structures. The trained neural network was then applied to 8 data sets, and the results were compared with expertly traced structures for validation purposes. Comparing several reliability metrics, including a relative overlap, similarity index, and intraclass correlation of the ANN generated segmentations to a manual trace are similar or higher to those measures previously developed methods. The ANN provides a level of consistency between subjects and time efficiency comparing human labor that allows it to be used for very large studies.

Keywords

Brain Segmentation, Feature Vectors, MRI, Neural Network

Pages

viii, 81 pages

Bibliography

Includes bibliographical references (pages 78-81).

Copyright

Copyright 2009 Eun Young Kim

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