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Multimodal retinal imaging: Improving accuracy and efficiency of image registration using Mutual Information

Legg, Philip Alexander 2010. Multimodal retinal imaging: Improving accuracy and efficiency of image registration using Mutual Information. PhD Thesis, Cardiff University.

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Abstract

This thesis addresses the challenging task of multi-modal image registration. Registration is often required in a number of applications, whereby two images are aligned to give matching correspondence between the features in each image. Such techniques have become popular in many different fields, especially in medical imaging. Multi-modal registration would allow for anatomical structure to be studied concurrently in both modalities, providing the clinician with a greater insight of the patient's condition. Glaucoma is a serious condition that damages the optic nerve progressively, leading to irreversible blindness. The disease can be treated so to prevent any further infection, however it can not be reversed. Therefore it is paramount that the disease is detected in the early stages so to minimise the affect of the condition. The work in this thesis focuses on two particular imaging modalities: colour fundus photographs and scanning laser ophthalmoscope images. Both images are captured from the human eye and show the appearance and reflectivity of the retina respectively. Registration of these two modalities would significantly improve demarcation and monitoring of the optic nerve head, a crucial stage for glaucoma diagnosis. In recent years, Mutual Information has become a popular technique used to perform multi-modal registration. This thesis provides a comprehensive overview of the algorithm. Firstly, an investigation is performed that shows how probability estimation can improve the algorithm performance. Secondly, the weaknesses of the current technique are revealed and so a novel solution is proposed that overcomes these problems. Finally, the proposed solution is incorporated in a non-rigid registration scheme that provides excellent registration accuracy for our intended application.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISBN: 9781303218316
Funders: Richard Whipp Scholarship
Date of First Compliant Deposit: 30 March 2016
Last Modified: 17 Jun 2017 08:32
URI: http://orca-mwe.cf.ac.uk/id/eprint/54992

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