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An Instinctive Application of Spatially Weighted Possibilistic Clustering Methods for the Detection of Lesions in Diabetic Retinopathy Images in Multi-dimensional Kernel Space

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Diabetic Retinopathy is a result of microvascular changes linked with hyperglycemia. The pathology is characterized by swollen and weak blood capillaries resulted due to excess glucose absorption by endothelial walls of them. The swollen and thick capillaries may burst and exude protein and fat based particles into the retinal fundus, which get clotted and obstruct the vision flow into the eye. The clotted particles are called Exudates, whose volume and number will decide the severity of the condition. As a result new blood capillaries are triggered to nourish the retina. But they are weak and fragile and may eventually burst making the complication more critical. Eye diagnosis requires dilation of the pupa using mydriatic drops which tends to many temporary and prolonged side effects. Also the reliability of the diagnosis is dependent on the Ophthalmologists experience and require hectic labor and time. This paper presents an instinctive multi-dimensional kernel possibilistic c means clustering method with induced spatial constraint for the detection of retinal abnormalities. The proposed method helps the clinician by automating the lesion detection process and has proven its statistical significance compared to the existing state of art methods.

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[Image Courtesy: Eye7 eye hospitals, New Delhi]

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The authors are indebted to Dr. N. Praveen Kanth Reddy, M.B.B.S, M.S (Ophthalmology), Suthrama Eye hospital, Madanapalle, for his esteemed endorsements, which helped to carry out this work successfully.

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Correspondence to R. Ravindraiah.

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Ravindraiah, R., Reddy, S.C.M. An Instinctive Application of Spatially Weighted Possibilistic Clustering Methods for the Detection of Lesions in Diabetic Retinopathy Images in Multi-dimensional Kernel Space. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07186-5

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  • Kernel metrics
  • Spatial information
  • Fuzzy C means (FCM) clustering
  • Possibilistic C means (PCM) clustering