Keratoconus Detection based on Corneal Morpho-Geometric Analysis using Correlation

The main challenge in the keratoconus detection algorithm uses a more number of parameters in analysis, which makes complexity in implementation and testing conditions. A corneal ecstatic disorder affects the corneal surface distortions on both the posterior and anterior surfaces of the human eye. The surface distortions show a lot of irregular levels as the format of the pattern. This distortion analyzes using corneal geometry. The proposed method detects the irregular level of keratoconus using the model of corneal in a topography way named as the corneal topography (CT). The basic parameters and the variables related to the geometric features of the eye. The level of distortion is estimated using three-dimensional modeling (3D) views, which create a new way for the diagnosis in the clinical method. The corneal topographic analysis measurement is executed by averaging the values of the posterior study. The main focus of the proposed method is to detect the surface present in the posterior portion of corneal distortion during the early period of the disease known as keratoconus. The correlation present in the estimated values between the posterior and anterior corneal surface, which be the low value of correlation. This idea is simulated using Matlab software.