Abstract
Diabetic Retinopathy (DR) is one of the important causes of blindness in diabetic patients. Diabetes that affects the retina is called diabetic retinopathy. Diabetic retinopathy occurs due to the damage of blood vessels in retina and increase in the level of glucose. Different pathologies are normally seen in DR such as microaneurysms, hard exudates, soft exudates, cotton wool spots and haemorrhages. We have done a comparative study of Hidden Markov Random Field (HMRF) and Gaussian Mixture Model (GMM) based HMRF for automatic segmentation of exudates and the performance analysis of both methods. The preprocessing consists of candidate extraction step using greyscale morphological operation of closing and initial labelling of exudates using K-means clustering followed by contour detection. In contour detection, we have analysed two approaches, one is GMM-based HMRF and the other is HMRF. DIARETDB1 is the dataset used.
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Revathi Achan, E., Swapna, T.R. (2019). Hidden Markov Random Field and Gaussian Mixture Model Based Hidden Markov Random Field for Contour Labelling of Exudates in Diabetic Retinopathy—A Comparative Study. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_123
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DOI: https://doi.org/10.1007/978-3-030-00665-5_123
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