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ObjectivesThe retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier.MethodsWe propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image.ResultsWe used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%.ConclusionsOur results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.

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Retinal arterioles were narrower in young adults with GOD compared with normal, suggesting that the presence of GOD might be associated with subclinical changes in retinal vascularization even in the absence of increased intraocular pressure. However, the clinical significance of these findings deserves further studies.