Automatic identification of anterior segment eye abnormality

Abstract

The eyes are complex sensory organs and are designed to optimize vision under conditions of varying light. There are a number of eye disorders that can influence vision. Eye disorders among the elderly are a major health problem. With advancing age, the normal function of eye tissues decreases and there is an increased incidence of ocular pathology. The most common symptoms elicited from ocular diseases are few in number and non-specific in nature: blurred vision, pain, and redness. Cataracts occur most frequently in older people and have significant impact on an individual's quality of life. There are effective therapies and visual aids for these potential vision-limiting conditions. Corneal haze a complication of refractive surgery is characterized by the cloudiness of the normally clear cornea. Iridocyclitis is the inflammation of the Iris and ciliary body. In corneal arcus are white circles in the cornea of the eye caused by fatty deposits. So, there is a need to diagnose to the normal eye from the abnormal one. This paper presents an identification of normal eye image and abnormal (consists of five kinds of eye images) classes using radial basis function (RBF) classifier. The features are extracted from the raw images using the image processing techniques and fuzzy K-means algorithm. Our system uses 150 subjects, consisting of five different kinds of eye disease conditions. We demonstrated a sensitivity of 90%, for the classifier with the specificity of 100%. Our systems are ready clinically to run on large amount of data sets.