D. R. Bull

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AIMS To develop and describe an objective classification system for the spatial patterns of visual field loss found in glaucoma. METHODS The 560 Humphrey visual field analyser (program 24-2) records were used to train an artificial neural network (ANN). The type of network used, a Kohonen self organising feature map (SOM), was configured to organise the(More)
There have been several reports on the application of artificial neural networks (ANNs) to visual field classification. While these have demonstrated that neural networks can be used with good results they have not explored the effects that the training set can have upon network performance nor emphasized the unique value of ANNs in visual field analysis.(More)
The increased interest in image fusion (combining images of two or more modalities such as infrared and visible light radiation) has led to a need for accurate and reliable image assessment methods. Previous work has often relied upon subjective quality ratings combined with some form of computational metric analysis. However, we have shown in previous work(More)
This paper reports on the application of an artificial neural network to the clinical analysis of ophthalmological data. In particular a 2-dimensional Kohonen self-organising feature map (SOM) is used to analyse visual field data from glaucoma patients. Importantly, the paper addresses the problem of how the SOM can be utilised to accommodate the noise(More)
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