Abstract
Hypertensive retinopathy is a disorder that causes hypertension which includes abnormalities in the retina that triggers vision problems. An active automated identification and categorizing of the hypertensive retinopathy would be extremely beneficial in the health system. The hypertensive cases will help to predict the cardiovascular diseases, and this prediction would help to save the people from the cardiovascular high-risk mortalities. This chapter presents an improved nearest neighbor distance clustering algorithm by recognizing the lesions present in the retina. The current approach identifies the symptoms associated with retinopathy for hypertension and classifies the hypertensive retinopathy. This chapter gives an assessment on hypertensive retinopathy recognition techniques that apply a range of image processing procedures, used for feature extraction and classification. The chapter also emphasizes the existing open databases, containing eye fundus images, which can be presently employed in the hypertensive retinopathy research.
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