Abstract
Computer aided diagnosis systems are very popular now days as they assist doctors in early detection of the disease. Diabetic maculopathy is one such disease which affects the retina of the diabetic patients. It affects the central vision of the person and causes blindness in severe cases. In this paper, an automated system for the grading of diabetic maculopathy has been developed, that will assist the ophthalmologists in early detection of the disease. Here, we propose a novel computerized method for the grading of diabetic maculopathy in fundus images. Our proposed system comprises of preprocessing of retinal image followed by macula and exudate regions detection. This is followed by feature extractor module for the formulation of feature set. SVM classifier is then used to grade the diabetic maculopathy. The publicly available fundus image database MESSIDOR has been used for the validation of our algorithm. The results of our proposed system have been compared with other methods in the literature in terms of sensitivity and specificity. Our system gives higher values of sensitivity and specificity as compared to others on the same database.
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MESSIDOR Database: http://messidor.crihan.fr/index-en.php
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Akram, M.U., Akhtar, M., Javed, M.Y. (2012). An Automated System for the Grading of Diabetic Maculopathy in Fundus Images. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_5
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DOI: https://doi.org/10.1007/978-3-642-34478-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34477-0
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