• Edward James School of Informatics and Computing, Indiana University Bloomington
  • Antonio Francisco School of Informatics and Computing, Indiana University Bloomington
Keywords: Image segmentation, Pattern Recognition, Supervised classification, Ocular Fundus, Retinal image analysis


 Information about the retinal blood vessel network is important for diagnosis, treatment, screening, evaluation and the clinical study of many diseases such as diabetes, hypertension and arteriosclerosis. Automated segmentation and identification of retinal image structures had become one of the major research subjects in the fundus imaging and diagnostic ophthalmology. Automatic segmentation of blood vessels from retinal images is considered as first step in development of automated system for ophthalmic diagnosis. With the development of computational efficiency, the pattern classification and image processing techniques are increasingly used in all fields of medical sciences particularly in ophthalmology. In this paper, we have presented a review of supervised classification algorithms for retinal vessel segmentation available in the literature. We outline the principles upon which retinal vessel segmentation algorithms are based. We discuss current supervised classification techniques used to automatically detect the blood vessels. 


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