Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656750
Title: Patch-based image analysis : application to segmentation and disease classification
Author: Tong, Tong
ISNI:       0000 0004 5349 3566
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
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Abstract:
In recent years, image analysis using local patches has received significant interest and has been shown to be highly effective in many medical imaging applications. In this work, we investigate machine learning methods which utilize local patches for different discriminative tasks. Specifically, this thesis focuses mainly on the applications of medical image segmentation in different imaging modalities as well as the classification of AD by using patch based image analysis. The first contribution of the thesis is a novel approach for the segmentation of the hippocampus in brain MR images. This approach utilizes local image patches and introduces dictionary learning techniques for supervised image segmentation. The proposed approach is evaluated on two different datasets, demonstrating competitive segmentation performance compared with state-of-the-art techniques. Furthermore, we extend the proposed approach for segmentation of multiple structures and evaluate it in the context of multi-organ segmentation of abdominal CT images. The second contribution of this thesis is a new classification framework for the detection of AD. This framework utilizes local intensity patches as features and constructs patch-based graphs for classification. Images from the ADNI study are used for the evaluation of the proposed framework. The experimental results suggest that not only patch intensities but also the relationships among patches are related to the pathological changes of AD and provide discriminative information for classification.
Supervisor: Rueckert, Daniel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.656750  DOI: Not available
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