Parametric characterization of the form of the human pupil from blurred noisy images.

The fluctuation of the human pupil is an important parameter in order to make non-invasive diagnosis of many different diseases and in several clinical applications. The relevant measurement device, the pupillometer, consists in a CCD camera, which shoots the pupil. We suppose that the measured image is blurred by a Gaussian kernel and corrupted by an additive white noise; moreover an elliptic shape for the pupil is assumed. We here present the extension of a multiscale approach for edge detection to identify some parameters of the pupil: the location of its centre, the length of the semi-axes and the orientation of the corresponding ellipse. The chosen method requires knowledge about the degradation parameters of the assumed model; so we first present a simple but efficient method to determine such quantities for the measured image. Then we apply the edge detection procedure to identify points close to the pupil edge, within a chosen probability. Finally we find the optimal ellipse fitting a suitable subset of the previously detected edge points. Results are presented, with comparisons to other approaches for edge finding.