images retinal blood vessels from digital colour fundus Automated localisation of the optic disc , fovea , and

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) The patterns of disease that aVect the fundus of the eye are varied and usually require identification by a trained human observer such as a clinical ophthalmologist. The employment of digital fundus imaging in ophthalmology provides us with digitised data that could be exploited for computerised detection of disease. Indeed, many investigators use computerised image analysis of the eye, under the direction of a human observer. The management of certain diseases would be greatly facilitated if a fully automated method was employed. An obvious example is the care of diabetic retinopathy, which requires the screening of large numbers of patients (approximately 30 000 individuals per million total population ). Screening of diabetic retinopathy may reduce blindness in these patients by 50% and can provide considerable cost savings to public health systems. 9 Most methods, however, require identification of retinopathy by expensive, specifically trained personnel. A wholly automated approach involving fundus image analysis by computer could provide an immediate classification of retinopathy without the need for specialist opinions. Manual semiquantitative methods of image processing have been employed to provide faster and more accurate observation of the degree of macula oedema in fluorescein images. Progress has been made towards the development of a fully automated system to detect microaneurysms in digitised fluorescein angiograms. 16 Fluorescein angiogram images are good for observing some pathologies such as microaneurysms which are indicators of diabetic retinopathy. It is not an ideal method for an automatic screening system since it requires an injection of fluorescein into the body. This disadvantage makes the use of colour fundus images, which do not require an injection of fluorescein, more suitable for automatic