Abstract
Diabetic retinopathy is an ophthalmic inflammation caused by diabetes which ends in visual defacement if not diagnosed earlier and that has two types namely Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). NPDR features are present in the earliest stage and systematic detection of these features can improve the diagnosis of the disease severity formerly. Several detection methods exists previously, still there is performance lack on large datasets. The objective of this study is detecting NPDR features from diabetic retinaopathy fundus images of large datasets with good performance level. The study has investigated different fuzzy based systems and to execute the objective; GK_FCM approach is proposed which integrates Gaussian Kernel function in conventional FCM. The execution has four phases, initially the input image undergoes preprocessing using green channel extraction, median filter to enhance the image quality and background removal is performed with extended minima transform technique, mathematical arithmetic operation and pixel replacement method to remove the outlier called Fovea (FV).
Highlights
Diabetes mellitus ordinarily referred to as diabetes is a protracted disease that occurs when the pancreas is no longer able to create insulin, so the glucose in the blood are not being transferred into cells, which leads to high blood glucose
Considering the importance of the disease severity and the complexity of the manual grading method, an emphasized screening system have to be developed with integrated and hybrid methods for accomplishing accurate diagnosis of the disease. This proposed work detects the first type of Diabetic Retinopathy (DR) disease called Non-Proliferative Diabetic Retinopathy (NPDR) with its features from retinal fundus images
There are a number of challenges in distinguishing and categorizing DR features; such as the presence of noise and outliers like the blood vessel, optic disc and fovea that are present in input images, the vacillating location of features, the similarity of shape and texture among some deformations, which may direct to extracting redundant or ineffective features and results in low segmentation accuracy
Summary
This organ is emphasized very less in healthcare. There is no awareness among people about related complications like blindness caused due to diabetes. The tenacious high blood glucose level famishes the small blood vessels with in the retina due to an improper supply of oxygen This distortion to the retinal part of human eyes due to diabetes is called “Diabetic Retinopathy”, which results in cloudy or blurred vision, and it is caused possibly among people with all types of diabetes such as type 1, type 2 and gestational. Considering the importance of the disease severity and the complexity of the manual grading method, an emphasized screening system have to be developed with integrated and hybrid methods for accomplishing accurate diagnosis of the disease This proposed work detects the first type of Diabetic Retinopathy (DR) disease called Non-Proliferative Diabetic Retinopathy (NPDR) with its features from retinal fundus images. The segmented features are dappled in the original input image using a multi-class contour tracking algorithm with different contouring measures as a post-processing operation
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