Fundus Image Analysis to Detect Abnormalities in Diabetic Retinopathy using Computer Aided Design Tools – A Review
R. Lavanya1, G. K. Rajini2
1R.Lavanya1*, Department of ECE, NBKRIST/Research Scholar, VIT, Vellore, India.
2G. K. Rajini, Department of Instrumentation, VIT, School of Electrical Engineering, Vellore, India.
Manuscript received on July 02, 2020. | Revised Manuscript received on July 10, 2020. | Manuscript published on August 30, 2020. | PP: 224-232 | Volume-9 Issue-6, August 2020. | Retrieval Number: C6366029320/2020©BEIESP | DOI: 10.35940/ijeat.C6366.089620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetic retinopathy is becoming a major threat to visual loss in human beings. Many researchers are working to develop early detection techniques, which may reduce the risk of vision loss using image-processing techniques like image enhancement and segmentation. Improving the quality of medical images to detect the disease at an early stage is crucial for further medication. It is gaining more focus with automated techniques for machine learning. Filtering and morphological operators enhance image contrast and interested region can be extracted using segmentation techniques from the fundus image of the retina. For feature analysis the optical disk, localization of blood vessels and segmentation are very useful to observe the parameters like area, length and perimeter of blood vessels etc. Algorithms for this analysis include preprocessing, segmentation, feature extraction and classification. This paper tries to give a detailed review of various image-processing methods used in early detection of diabetic retinopathy and future insights to develop algorithms, which reduces clinician’s time for diagnosis and pathogenesis.
Keywords: Diabetic retinopathy, Image enhancement, Pre-processing, IRE, Microanueurysm, Segmentation, Feature Extraction, Machine learning