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Impact of MRI technology on Alzheimer's disease detection

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thesis
posted on 28.03.2022, 23:38 by Saruar Alam
Alzheimer's disease (AD) can be detected using magnetic resonance imaging (MRI) based features and supervised classifiers. The subcortical and ventricular volumes change for AD patients. These volumes can be extracted from MRI by tools such as Free Surfer and multi-atlas-based likelihood fusion (MALF) algorithm. Medical imaging centers typically use MRI protocols for brain scanning.These protocol differences include different scanner models with various operating parameters. The scanner models can have the same or different field strengths. A key factor in classifying multicentric MR subject images having different protocols is how different scanner models affect the extraction of features, and subsequent classification performance of a supervised classifier. We have investigated the classification performance of FreeSurfer and MALF based volume features together with Radial Basis Function Support Vector Machine and Extreme Learning Machine across different imaging protocols. We have also investigated both FreeSurfer and MALF, whose defined regions of the brain are most effective for the detection of the disease over different protocols. Our study result indicates marginal differences in classification performance across scanner models with the same or different field strengths when differentiating AD, Mild Cognitive Impairment, and Normal Controls.We have also observed differences in ranking order of the most effective regions.

History

Table of Contents

Statement of Originality -- Abstract -- Table of contents -- List of figures -- List of tables -- Acknowledgement -- Alzheimer's Disease Neuroimaging Initiative (ADNI) Acknowledgement -- 1. Introduction -- 2. Background and related works -- 3. AD Diagnostic models -- 4. Data and experimental work -- 5. Result and discussion -- 6. Conclusion

Notes

Bibliography: pages 54-60 Theoretical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Science, Department of Computing

Department, Centre or School

Department of Computing

Year of Award

2018

Principal Supervisor

Len Hamey

Additional Supervisor 1

Keven Ho-Shon

Rights

Copyright Saruar Alam 2018 Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (69 pages)

Former Identifiers

mq:71181 http://hdl.handle.net/1959.14/1271708