Aim-To recognise automatically the main components of the fundus on digital colour images. Methods-The main features of a fundus retinal image were defined as the optic disc, fovea, and blood vessels. Methods are described for their automatic recognition and location. 112 retinal images were preprocessed via adaptive, local, contrast enhancement. The optic discs were located by identifying the area with the highest variation in intensity of adjacent pixels. Blood vessels were identified by means of a multilayer perceptron neural net, for which the inputs were derived from a principal component analysis (PCA) of the image and edge detection of the first component of PCA. The foveas were identified using matching correlation together with characteristics typical of a fovea-for example, darkest area in the neighbourhood of the optic disc. The main components of the image were identified by an experienced ophthalmologist for comparison with computerised methods. Results-The sensitivity and specificity of the recognition of each retinal main component was as follows: 99.1% and 99.1% for the optic disc; 83.3% and 91.0% for blood vessels; 80.4% and 99.1% for the fovea. Conclusions-In this study the optic disc, blood vessels, and fovea were accurately detected. The identification of the normal components of the retinal image will aid the future detection of diseases in these regions. In diabetic retinopathy, for example, an image could be analysed for retinopathy with reference to sight threatening complications such as disc neovascularisation, vascular changes, or foveal exudation. (Br J Ophthalmol 1999;83:902-910)

Fully automated computer algorithms were able to detect hard exudates and HMA. This paper presents encouraging results in automatic identification of important features of NPDR.

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Intraocular lenses made from AcrySof were associated with a significantly reduced degree of PCO.

This system could be used when screening for diabetic retinopathy. At 94.8% sensitivity setting the number of normal images requiring examination by a human grader could be halved.

This study showed that the cross-sectional area of retinal tissue between the plexiform layers in cystoid macular edema, as imaged by OCT, is the best indicator of visual function at baseline. Further prospective treatment trials are needed to investigate this parameter as a predictor of visual outcome after intervention.

The rate of PCO was significantly higher with the hydrophilic IOL. However, the results cannot be attributed to the IOL material alone as they show the importance of both IOL material and design.

A two stage method of image segmentation based on gray level cooccurrence matrices is described. An analysis of the distributions within a cooccurrence matrix defines an initial pixel classification into both region and interior or boundary designation. Local consistency of pixel classification is then implemented by minimising the entropy of local information, where region information is expressed via conditional probabilities, estimated from the cooccurrence matrices, and boundary information via conditional probabilities which are determined a priori. The method robustly segments an image into homogeneous areas and generates an edge map. The technique extends easily to general edge operators; an example is given for the Canny operator. Applications to synthetic and forward looking infrared (FLIR) images are given.