Automatic Detection of Diabetic Retinopathy Using Image Processing Techniques

2022 ◽  
pp. 132-142
Author(s):  
Mihai Ciobancă ◽  
Simina Emerich ◽  
Mircea Giurgiu
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Automatic Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Retinal vessel feature extraction from fundus image using image processing techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 687
Author(s):  
R. Lavanya ◽  
G. K. Rajini ◽  
G. Vidhya Sagar
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Blood Vessels ◽  
Retinal Vessel ◽  
Retinal Images ◽  
Image Processing Techniques ◽  
Vessel Detection ◽  
Proper Diagnosis ◽  
Processing Techniques

Retinal Vessel detection for retinal images play crucial role in medical field for proper diagnosis and treatment of various diseases like diabetic retinopathy, hypertensive retinopathy etc. This paper deals with image processing techniques for automatic analysis of blood vessel detection of fundus retinal image using MATLAB tool. This approach uses intensity information and local phase based enhancement filter techniques and morphological operators to provide better accuracy.Objective: The effect of diabetes on the eye is called Diabetic Retinopathy. At the early stages of the disease, blood vessels in the retina become weakened and leak, forming small hemorrhages. As the disease progress, blood vessels may block, and sometimes leads to permanent vision loss. To help Clinicians in diagnosis of diabetic retinopathy in retinal images with an early detection of abnormalities with automated tools.Methods: Fundus photography is an imaging technology used to capture retinal images in diabetic patient through fundus camera. Adaptive Thresholding is used as pre-processing techniques to increase the contrast, and filters are applied to enhance the image quality. Morphological processing is used to detect the shape of blood vessels as they are nonlinear in nature.Results: Image features like, Mean and Standard deviation and entropy, for textural analysis of image with Gray Level Co-occurrence Matrix features like contrast and Energy are calculated for detected vessels.Conclusion: In diabetic patients eyes are affected severely compared to other organs. Early detection of vessel structure in retinal images with computer assisted tools may assist Clinicians for proper diagnosis and pathology. 

scholarly journals Detection of Diabetic Retinopathy by Retinal Screening using Image Processing Techniques

2020 ◽  
pp. 447-457
Author(s):  
Shaziya Banu S ◽  
Ravindra S
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Nearest Neighbor ◽  
Extreme Condition ◽  
Diabetic Patients ◽  
K Nearest Neighbor ◽  
Image Processing Techniques ◽  
Primary Driver ◽  
Processing Techniques

<p>Diabetic Retinopathy (DR) is a related malady with diabetes and primary driver of sightlessness in diabetic patients. Epidemiological overview categorizes DR among four significant reasons for sight impedance. DR is a microvascular entanglement in which meager retinal veins may blast, bringing about vision misfortune. In this condition veins in retina swells and may blast in severe extreme condition. Operative medication is timely discovery by steady screenings that is by emphasizing the determination of retinal images using appropriate image processing techniques such as, Preprocessing of retinal image, image segmentation using sobel edge detector, local features extraction like mean, standard deviation, variance, Entropy, histogram values and so on. For classification of retina, system uses K-Nearest Neighbor (KNN) classifier. By adopting this approach, The classification of normal and abnormal images of retina is easy and will reduce the number of reviews for the ophthalmologists. Developing a method to automate functionality of retinal examination helps doctor to identify patient’s condition on disease. So that they can medicate the disease accordingly.</p>

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