Managing the noisy glaucomatous test data by self organising maps

One of the main difficulties in obtaining reliable data from patients in glaucomatous tests is the measurement noise caused by the learning effect, inattention, failure of fixation, fatigue, etc. Using Kohonen´s self-organising feature maps, we have developed a computational method to distinguish between the noise and true measurement. This method has been shown to provide a satisfactory way of locating and rejecting noise in the test data, an improvement over conventional statistical methods