Error analysis of fundus image registration using quadratic model transfformation

Feature-based registration of retinal images proved to be very successful especially for minimally overlapping images. The most commonly used transformation method uses a quadratic model to represent the geometry of the retinal surface. Although this model has been used for more than one decade, there is no literature that studies the model errors for abnormal eye geometries. In this work, we present a study of the registration errors of the quadratic model in case of diseased eyes. The study includes two basic models of the retinal surface for eyes suffering from: myopia; and retinal diseases (e.g. age related macular degeneration). In addition, real datasets of age related macular degeneration (AMD) patients have been used to quantify the registration error. The simulation results show that the average error can be as high as 13 pixels at extreme conditions of myopia and retinal diseases. For real datasets with typical disease conditions, the error was found to be 2.6 pixels.