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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:656750 |
| Date | January 2014 |
| Creators | Tong, Tong |
| Contributors | Rueckert, Daniel |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/24453 |
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