Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis

Author:

Siva Sundhara Raja D.1,Vasuki S.2

Affiliation:

1. Department of ECE, SACS MAVMM Engineering College, Madurai, Tamil Nadu 625 301, India

2. Department of ECE, Velammal College of Engineering and Technology, Madurai, Tamil Nadu 625 009, India

Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss in diabetic patients. DR is mainly caused due to the damage of retinal blood vessels in the diabetic patients. It is essential to detect and segment the retinal blood vessels for DR detection and diagnosis, which prevents earlier vision loss in diabetic patients. The computer aided automatic detection and segmentation of blood vessels through the elimination of optic disc (OD) region in retina are proposed in this paper. The OD region is segmented using anisotropic diffusion filter and subsequentially the retinal blood vessels are detected using mathematical binary morphological operations. The proposed methodology is tested on two different publicly available datasets and achieved 93.99% sensitivity, 98.37% specificity, 98.08% accuracy in DRIVE dataset and 93.6% sensitivity, 98.96% specificity, and 95.94% accuracy in STARE dataset, respectively.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modelling and Simulation,General Medicine

Cited by 35 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Morphology Approach for Segmentation of Blood Vessels in Retinal Images;2023 6th International Conference of Computer and Informatics Engineering (IC2IE);2023-09-14

2. Classification of Ocular Diseases Related to Diabetes Using Transfer Learning;International Journal of Online and Biomedical Engineering (iJOE);2023-08-16

3. A Study of Effective Screening Methods for Grading Diabetic Retinopathy using Mathematical Approaches;2022 3rd International Conference on Smart Electronics and Communication (ICOSEC);2022-10-20

4. Retinal Blood Vessel Extraction From Fundus Images Using Improved Otsu Method;Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention;2022-09-09

5. A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images;International Journal of Online and Biomedical Engineering (iJOE);2022-02-16

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