Analysis of retinal images in glaucoma.

Glaucoma is a leading cause of visual disability. Confocal scanning laser tomography (CSLT) yields reproducible three-dimensional images of the optic nerve head and is widely used in the assessment of the disease. The real promise of this technology may be in evaluating progressive structural deterioration in the optic nerve head (ONH) associated with glaucoma over a patient’s follow-up. This might be possible as the measurements from the technology have been shown to be sufficiently reproducible. The purpose of this thesis is twofold: to investigate statistical techniques for detecting progressive structural glaucomatous damage; and to investigate techniques which improve the repeatability of images obtained from the technology. Proven quantitative techniques, collectively referred to as statistic image mapping (SIM) are widely used in neuro-imaging. In this thesis some of these techniques are adapted and applied to series of ONH images. The pixel by pixel analysis of topographic height over time yields a ‘change map’ flagging areas and intensity of active change in series of ONH images. The technique is compared to the Topographic Change Analysis (TCA superpixel analysis) and to change in summary measures of the three-dimensional ONH (‘stereometric parameters’). The comparisons are made using a novel computer simulation developed in this thesis and further tested on clinical data. A false-positive rate was recorded using testretest data obtained from 74 patients with ocular hypertension (OHT) or glaucoma. A true-positive rate was estimated using a longitudinal dataset of 52 OHT patients classified as having progressed by visual fields during follow-up. Maximum Likelihood (ML) deconvolution is an image processing technique which estimates the original scene from a degraded image using maximum likelihood probability. This technique has been used in other confocal applications to remove ‘out-of-focus’ haze and noise in 3D confocal data. In this thesis the approach is applied to testretest series to evaluate if the technique improves the repeatability of image series. Computer simulation indicated that SIM has better diagnostic precision than TCA in