Segmentation of retina images to detect abnormalities arising from diabetic retinopathy
by Amrita Roy Chowdhury; Sreeparna Banerjee
International Journal of Image Mining (IJIM), Vol. 4, No. 1, 2021

Abstract: Segmentation of retina images to isolate and detect abnormalities is an important step before the classification can be performed. In this paper, we apply three popular unsupervised segmentation algorithms, namely, K-means clustering, fuzzy C-means clustering and Otsu multilevel thresholding algorithm to extract dark and bright lesions caused by diabetic retinopathy, in the earlier stages of its prognosis. This segmentation process also helps in removing normal structures in the retina images like optic disc and blood vessel tree. The results of the best performing segmentation algorithm can subsequently be used in classification and thereby aid the ophthalmologists in diagnosing the disease. It is found that while Otsu segmentation performs best, K-means is a close second and outperforms fuzzy C-means clustering in terms of time complexity and is therefore the best choice.

Online publication date: Mon, 21-Jun-2021

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