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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/19579

Title: Computer aided analysis of inflammatory muscle disease using magnetic resonance imaging
Authors: Jack, James
Keywords: Computer aided diagnosis
Computer aided detection
Machine learning
Issue Date: 2015
Publisher: © James V. Jack
Abstract: Inflammatory muscle disease (myositis) is characterised by inflammation and a gradual increase in muscle weakness. Diagnosis typically requires a range of clinical tests, including magnetic resonance imaging of the thigh muscles to assess the disease severity. In the past, this has been measured by manually counting the number of muscles affected. In this work, a computer-aided analysis of inflammatory muscle disease is presented to help doctors diagnose and monitor the disease. Methods to quantify the level of oedema and fat infiltration from magnetic resonance scans are proposed and the disease quantities determined are shown to have positive correlation against expert medical opinion. The methods have been designed and tested on a database of clinically acquired T1 and STIR sequences, and are proven to be robust despite suboptimal image quality. General background information is first introduced, giving an overview of the medical, technical, and theoretical topics necessary to understand the problem domain. Next, a detailed introduction to the physics of magnetic resonance imaging is given. A review of important literature from similar and related domains is presented, with valuable insights that are utilised at a later stage. Scans are carefully pre-processed to bring all slices in to a common frame of reference and the methods to quantify the level of oedema and fat infiltration are defined and shown to have good positive correlation with expert medical opinion. A number of validation tests are performed with re-scanned subjects to indicate the level of repeatability. The disease quantities, together with statistical features from the T1-STIR joint histogram, are used for automatic classification of the disease severity. Automatic classification is shown to be successful on out of sample data for both the oedema and fat infiltration problems.
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
Sponsor: Computer Science, Loughborough University
URI: https://dspace.lboro.ac.uk/2134/19579
Appears in Collections:PhD Theses (Computer Science)

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