Knowledge Mining In The Clinical Assessment Of Glaucoma

Zhu, H. (2010). Knowledge Mining In The Clinical Assessment Of Glaucoma. (Unpublished Doctoral thesis, City, University of London)

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Abstract

Glaucoma is a leading cause of irreversible blindness and visual impairment. In the clinic, glaucomatous damage can be characterized by structural changes in the optic nerve head (ONH) and retinal nerve fibre layer (RNFL) that can be evaluated by various retinal-imaging techniques such as scanning laser polarimetry and optical coherence tomography (OCT). The structural damage can lead to functional damage in the visual field (VF), normally assessed with standard automated perimetry, which assesses the differential light sensitivity in the field of view. The clinical measurements of retinal structure and visual function play an important role in the detection and management of glaucoma, but the data generated is often complex and highly variable, thus making it hard to clinically interpret. The purpose of this thesis was to investigate knowledge mining procedures for extracting clinically useful information from these measurements. Knowledge mining describes iterative divide-and-conquer type analyses of large-scale questions: solutions to individual smaller problems are used to generate better quality knowledge, which in the case of work reported in this thesis can be translated into clinically useful analysis tools. This thesis describes five knowledge mining procedures specifically developed and applied to structural and functional measurements in glaucoma: (1) probabilistic inference to aid image acquisition of OCT images; (2) a robust and efficient segmentation algorithm to extract features of retina tissue layer structures in large-scale 3-dimensional image volumes acquired by OCT; (3) a predictive structure-function relationship model to bridge the retinal structure and visual function measurements in glaucoma; (4) quantification and visualization of structure-function discordance using the model about structure-function relationship; (5) integration of structural and functional measurements to improve the reproducibility of the measurements. In conclusion the knowledge mining approaches improved the acquisition and/or accuracy of the measurements and provide new clinical analysis tools to detect and manage glaucoma.

Item Type: Thesis (Doctoral)
Subjects: R Medicine
R Medicine > RE Ophthalmology
Divisions: School of Health Sciences
URI: http://openaccess.city.ac.uk/id/eprint/17567

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