Comparative Thresholding Analysis for Keratoconus Detection using Probability Density Function

Abstract

Generally the keratoconus indicates the condition in which the human eye’s cornea region unable to hold its round shapes. In cornea, the intensity points around the boundary   have drawbacks in predicting the irregular shape. The proposed method uses the techniques of intensity wise classification, edge detection method using the Sobel detector and the density based threholding. The feature vectors like test image and trained image are classified with the Probability Density Function (PDF). The keratoconus parameters to be model using the prediction model and the normal eye, the keratoconus outputs are classified using Probability Density Function. The outputs of the keratoconus are compared with the help of machine learning techniques.