Abstract
The paper aims to segment the lesion of non-proliferative diabetic retinopathy (NPDR) which occurred due to diabetes. The risk of loss of sight can be decreased by 95% with the timely diagnosis of the NPDR disease. The proposed work aimed to segment the NPDR lesion called ‘hard exudate’. Firstly, the fundus input image undergoes resizing by applying bi-cubic interpolation area method, and then the resized image is preprocessed with single channel extraction and median filter. Further, the NPDR lesion is segmented using k-means and fuzzy C-means (FCM) algorithms. By comparing the results of both segmentation algorithms, FCM shows the better result. The executions of the methods are evaluated using mean-squared error, structural similarity index measure, sensitivity, specificity and accuracy. The proposed FCM method for segmenting the ‘hard exudate’ lesion has achieved a better result of 95.05% accuracy.
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Shalini, Sasikala (2021). Fuzzy C-means for Diabetic Retinopathy Lesion Segmentation. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_17
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DOI: https://doi.org/10.1007/978-981-33-6862-0_17
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