Open AccessFundus Image Analysis to Detect Abnormalities in Diabetic Retinopathy using Computer Aided Design Tools A Review
Author(s) -
R Lavanya,
G. K. Rajini
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6366.089620
Subject(s) - computer science , artificial intelligence , segmentation , preprocessor , diabetic retinopathy , computer vision , fundus (uterus) , image processing , image segmentation , feature extraction , feature (linguistics) , feature detection (computer vision) , focus (optics) , pattern recognition (psychology) , image (mathematics) , medicine , ophthalmology , diabetes mellitus , linguistics , philosophy , physics , optics , endocrinology
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.