No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis studied the effect of (i) the number of grating components and (ii) parameter randomisation on root-mean-square (r.m.s.) contrast sensitivity and spatial integration. The effectiveness of spatial integration without external spatial noise depended on the number of equally spaced orientation components in the sum of gratings. The critical area marking the saturation of spatial integration was found to decrease when the number of components increased from 1 to 5-6 but increased again at 8-16 components. The critical area behaved similarly as a function of the number of grating components when stimuli consisted of 3, 6 or 16 components with different orientations and/or phases embedded in spatial noise. Spatial integration seemed to depend on the global Fourier structure of the stimulus. Spatial integration was similar for sums of two vertical cosine or sine gratings with various Michelson contrasts in noise. The critical area for a grating sum was found to be a sum of logarithmic critical areas for the component gratings weighted by their relative Michelson contrasts. The human visual system was modelled as a simple image processor where the visual stimuli is first low-pass filtered by the optical modulation transfer function of the human eye and secondly high-pass filtered, up to the spatial cut-off frequency determined by the lowest neural sampling density, by the neural modulation transfer function of the visual pathways. The internal noise is then added before signal interpretation occurs in the brain. The detection is mediated by a local spatially windowed matched filter. The model was extended to include complex stimuli and its applicability to the data was found to be successful. The shape of spatial integration function was similar for non-randomised and randomised simple and complex gratings. However, orientation and/or phase randomised reduced r.m.s contrast sensitivity by a factor of 2.

This thesis considers two distinct but related topics concerned with the analysis of images of the ophthalmic fundus. The first involved the differentiation of automatically detected retinal microaneurysms from other spurious objects. A review of the current literature indicated that there had been no rigorous comparison of automated methods of classification for this type of ophthalmological task. Three classification techniques were investigated; a rule based system, linear discriminant analysis and a learning vector quantizer artificial neural network. Each classifier was trained and tested on the same pair of datasets, and the results analysed using receiver operating characteristic curves. It was found for this application that the rule based system performed marginally better than the linear discriminant analysis approach and both were superior to the neural network method. Whilst the improved performance of the rule based system may, in this clinical diagnostic situation, justify its higher development effort, the simplicity and transparency of the statistical method had much to commend it. The second investigation explored methods of completely segmenting the retinal vessel structure from the fundus image. Although a number of studies have considered different parts of the problem, few integrated solutions have been proposed. Each of the steps required were identified and analysed with the objective of integrating them into a fully automated segmentation and analysis system. Some of the problems were successfully resolved but others were either not completely resolved or were shown to be intractable given the current visual description of the problem.