Analysis of pupil fluctuations after a light stimulus by image processing and neural network

In this paper the analysis of pupil fluctuations after a light stimulus is considered; it is useful for non-invasive diagnosis of many different diseases. When a light stimulus is presented to a subject, the pupil response is not instantaneous because of the action of the sphincter muscle. A sequence of images will be caught by a pupillometer and each image of the sequence will be binarized; for each segmented image, a useful parameter will be considered, the major diameter that is the length (in pixels) of the major axis of the ellipse that has the same second moments of the pupil. The aim is the identification of the response time after a light stimulus, from the sequence of the major diameters. The considered signal is degraded because of the presence of the measurement noise, the natural fluctuation of the pupil (usually called “pupil noise”), and the general health state of the subject. To enhance the significant part of this noisy signal a neural network is suitable trained. From the clean signal the identification of the response time of the pupil will be easier and a simple method will be proposed.