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dc.contributor.advisorDevaney, Nicholas
dc.contributor.authorRamaswamy, Gomathy
dc.date.accessioned2014-02-10T15:44:18Z
dc.date.available2014-02-10T15:44:18Z
dc.date.issued2014-01-27
dc.identifier.urihttp://hdl.handle.net/10379/4171
dc.description.abstractIn recent decades, adaptive optics (AO) technology has been embedded into retinal imaging devices, producing a new generation of instrument, which can provide retinal images with cellular resolution. This new technological advancement allows viewing of retinal microscopic structure, which is of great significance for the diagnosis, and subsequent treatment monitoring, of retinal pathologies that can result in visual loss. Developing compact and simplified AO assisted retinal imaging devices with an automated feature analysis is the current focus of interest for transferring AO technology to clinical use. In this study, we present an enhanced processing of sequences of retinal images obtained using an AO flood illumination system. We aim to provide image processing techniques for pre-processing, assessing the quality and image registration of cone photoreceptor and retinal nerve fiber layer (RNFL) images. Our results demonstrate the effectiveness of a wavelet based approach to correcting uneven illumination and automatic evaluation of image quality in terms of the results of image registration. In particular, we present the significance of image quality analysis while selecting a certain percentage of the sharpest images in a sequence for image registration. In order to register the images, we include methods that are specifically developed to measure tiny rotations in addition with correlation based techniques to correct for translational motion. We show that correcting for small rotations exhibits a significant improvement, especially at the edges of the image, which is important for creating larger mosaics. We then present the methods of investigating the characteristics of retinal nerve fiber bundles to discriminate RNFL images with good and poor striation. This enables feature comparison between healthy and glaucoma eyes. Finally, we discuss the implications of our results and possible future studies.en_US
dc.subjectOpthalmologyen_US
dc.subjectAdaptive opticsen_US
dc.subjectImage processingen_US
dc.subjectImage analysisen_US
dc.subjectApplied Opticsen_US
dc.subjectApplied Optics Departmenten_US
dc.subjectSchool of Physicsen_US
dc.titlePre-processing, Registration and Quality Assessment of Adaptive Optics Assisted Retinal Imagesen_US
dc.typeThesisen_US
dc.local.noteIn recent decades, the technique of adaptive optics (AO) has allowed us to examine the retina in greater detail than ever before. This will be of great benefit for the diagnosis and monitoring of the treatment of retinal disease. In this study, we develop image processing techniques to further enhance the images of the retina by correcting for non-uniform illumination, aligning images with high precision and allowing the sharpest images to be selected. The image alignment methods are particularly developed to measure tiny rotations of the eye in addition with translational motion. We show that correcting for small rotations exhibits a significant improvement, especially at the edges of the image, which is important for creating larger mosaics. In addition, we present methods to analyse the characteristics of retinal nerve fiber bundles in order to discriminate healthy eyes from those suffering glaucoma.en_US
dc.local.finalYesen_US
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