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Examining the variation of vascular structure in digital fundus images using textural pattern

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Abstract

One of the most significant retinal abnormality in which an individual loses the vision is diabetic retinopathy (DR). The appropriate way to treat this disease would be easier if it is detected at an earlier stage. The study on the vasculature extracted from illumination correction on the fundus image brings the presence of diabetic retinopathy. This preprocessing involves three steps. Initially illumination and reflectance estimation is done and then illumination correction is employed and finally the clipped histogram equalization is done to preserve the brightness of the image so that the information on the retinal image may not get saturated. Here, k-means segmentation process has been done and the local binary pattern (LBP) has been calculated. The selected feature vectors are then classified by using an echo state neural network (ESNN). The proposed method has been tested on publically available database DIARETDB1 that contained 89 DR fundus images in total. The result of detecting and classifying the pathology based on vasculature study on these images yielded sensitivity of 86.46%, specificity of 80.47%, and accuracy of 96.92%.

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Acknowledgements

We express our gratitude to the Department of Science and Technology (DST WOS Scheme) whom we availed the necessary funds and support for successfully implementing this idea.

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  1. Department of Electronics and Communication Engineering, CEG Campus, Anna University, Chennai, Tamilnadu, India

    M. TamilNidhi & K. Gunaseelan

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  1. M. TamilNidhi
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  2. K. Gunaseelan
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Correspondence to M. TamilNidhi.

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TamilNidhi, M., Gunaseelan, K. Examining the variation of vascular structure in digital fundus images using textural pattern. Pers Ubiquit Comput 22, 961–970 (2018). https://doi.org/10.1007/s00779-018-1169-7

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  • DOI: https://doi.org/10.1007/s00779-018-1169-7

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